JP2005020949A - Multi-inertia machine model estimating device for electric motor control device - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a multi-inertia machine model estimating device that can presume a machine model, as a multi-inertia model, to be utilized for servo adjustment, even though it does not have advanced skills. <P>SOLUTION: This multi-inertia machine-model estimating device for an electric motor control device, structured by providing a frequency characteristic arithmetic unit 10 on the electric motor control device that has a command unit 2, an electric motor controller 3, and a detecting means 5 of the operation data of a machine 6, is provided with a memory 11, in which the frequency characteristic arithmetic unit 10 stores frequency characteristics formulae; a frequency characteristic peak detecting portion; an attenuation estimated analyzing portion that presumes attenuation from a plurality of resonance frequencies and the like, detected by the frequency characteristic peak detecting portion; and a frequency characteristics difference calculating portion that calculates the differences from the frequency characteristics obtained by the measurements to each of the frequency characteristics of 2 to N pieces of inertia models (N: an integer of 2 or larger), calculated from the frequency characteristic formulae consisting of either one or more of a rigid body model, a plurality of oscillating models of the degrees of freedom, and a model of a single degree of influence by a higher-order frequency. <P>COPYRIGHT: (C)2005,JPO&NCIPI

Description

式（１）は、電動機のパラメータと機械共振系のパラメータが組み合わさっており、式を１次式と２次式の積に因数分解することは、非常に困難である。このため、式の物理的意味が不明確であり、制御系全体に対して状態オブザーバを構成せざるを得ない。
【０００５】
そこで従来技術では、式（３）によるＰ（ｓ）の近似式を求めている。
【数２】

【０００６】
【数３】

【０００７】
式（６）より、２慣性共振系は図１４の破線のブロック４０６内に示す等価剛体系Ｇ_１（ｓ）と機械共振系Ｇ_２（ｓ） とに分離できる。もし、等価剛体系の速度が検出できれば、観測出力から等価剛体系の検出速度を差し引いた差信号により、機械振動成分のみが検出できる。
等価剛体系の速度は仮想的な状態量であり、検出不可能である。状態オブザーバを用いると、等価剛体系の速度が観測可能であるが、状態オブザーバの設計理論を単純に適用するだけでは、オブザーバが機械共振系Ｇ_２（ｓ）を含まざるを得ない。
このため、従来技術では、機械系が等価剛体系と共振系とに分離できたことに着目し、等価剛体系は物理的に低域通過特性となり、高周波である共振成分には、ほとんど応答しないと考え、電動機の角速度（図１４で共振系の出力）を観測出力と近似して、等価剛体オブザーバ（等価剛体系をモデルとする状態オブザーバ）を構成している。
電動機のトルクに比例する変量をｕ（ｔ） に入力し、電動機の角速度をｙ（ｔ） に入力すると、機械共振信号

と、推定外乱信号

を得る構成が式（８）のように構成できる。
【０００８】
【数４】

従来技術の機械振動検出装置で設定しなければならないパラメータは、状態オブザーバの帯域ω_ｆ とＱ_ｆ 、および電動機を含む機構のパラメータを意味するγとＤ_０である。パラメータω_ｆ とＱ_ｆ は必要に応じて、自由に設定する。
電動機と負荷（伝達機構）の慣性モーメントと粘性摩擦係数は、計算あるいは測定から求め、パラメータγとＤ_０ を決め、または機械共振系の共振周波数と反共振周波数とを測定して、γを計算する。
【０００９】
従来技術の実施例を図１４により説明する。
図１４は、推定した等価剛体速度

を出力する等価剛体モデル手段と、比例演算を行う第１の補償手段と、積分演算を行う第２の補償手段とで構成される。
図１４において、電動機のトルクに比例する信号ｕ（ｔ） （例えば、電動機のトルク制御装置のトルクモニタ信号あるいはトルク指令信号等）と電動機の角速度である観測出力信号を機械振動検出装置４００に入力すると、機械共振信号と推定外乱信号を独立に出力する。
【００１０】
従来技術の第２の実施例を、図１５によって説明する。
図１５は、図１４と同様である機械振動検出装置４００と、積分要素あるいは一次遅れ要素で構成する第３の補償手段と、積分要素あるいは一次遅れ要素で構成する第４の補償手段と、制振フィードバックゲイン（Ｋｆ）手段と反転／非反転増幅手段と加算手段（図では加算点で示す）とからなる前記加算入力手段と、で構成されている。
機械振動検出装置４００が推定した機械共振信号を第３の補償手段に入力し、第３の補償手段の出力を第４の補償手段に入力し、その出力を加算増幅手段に入力する。前記加算手段は、第３および第４の補償手段と反転／非反転増幅手段を通った機械共振信号と上位の制御装置（例えば、速度制御用補償器）から出力されるトルク指令信号を加算して、電動機のトルク制御装置のトルク指令信号として出力する。前記電動機のトルク制御装置のトルク指令信号あるいは電動機のトルク制御装置のトルクモニタ信号を電動機のトルクに比例する信号ｕ（ｔ）として、機械振動検出装置に入力する。
ゲイン調整等を行えば、機械共振を抑えながら、速度制御系の周波数帯域を広げ得る。
以上のように、従来技術は、機械共振系を等価剛体系と共振系とに理論的に分離し、等価剛体系は物理的に低域通過特性となり、高周波である共振成分には、ほとんど応答しないという考えに基づいて、電動機の角速度（図１４の共振系の出力）を観測出力と近似することによって、等価剛体系をモデルとする状態オブザーバである等価剛体オブザーバを構成し、電動機の角速度信号と電動機のトルクに比例する信号とから機械振動成分と外乱成分とを独立に検出し、外乱成分の混入による機械共振信号の品質劣化を防止している。
また、従来技術は、等価剛体オブザーバに機械振動系のモデルを用いず、たとえ２慣性以上の共振現象を伴っていても、機械振動検出装置の次数は常に等価剛体モデルの次数＋１なので、計算量増大および設定すべきパラメータ数を増大させず、測定が困難な機械共振のパラメータを設定しない。
また、等価剛体オブザーバには、前記第１の補償手段（比例）と前記第２の補償手段（積分）とからなるフィードバックループを備え、検出した機械振動信号成分にオブザーバのパラメータ変動しにくくしている。
従来技術の機械共振検出装直によって、高周波の機械振動を検出し、制振装置は、高周波の機械振動抑制と、調整により速度制御系の広帯域化を行う。
【００１１】
【発明が解決しようとする課題】
しかしながら、従来技術は、電動機を含む機構系のモデル化が集中マスとバネと減衰から構成するようになっていて、マス−バネの数が多くなると、バネ・マスの組み合わせの数が増大するという問題があった。２慣性モデルではバネ、マス、減衰の組み合わせは１つだが、３慣性以上になると、２種類以上の組み合わせが存在する。そのため、多慣性になると、組み合わせが増大し、構成を確定できない問題があった。
また、バネ・マスの組み合わせ構成を確定できたとしても、慣性モーメントもしくは重量の総量は確定もしくは推定できても、多慣性にした複数のマスや、ばね剛性、減衰の分布値は不明である。
さらに、多慣性モデルの次数が大きくなると、式が複雑化するので、高次のモデル式を定義できない問題を事実上抱えている。
従来技術に準じて、電動機が発生するトルクから電動機の角速度（観測出力）までの周波数特性Ｈ_ｉ，ｊを多慣性化して数値化すると、式（１２）のようになり、
【数５】

各係数ａ_０，・・・ａ_ｍ，ｂ_０， ・・・，ｂ_２ｎを決定しなければならず、高度な専門知識、経験を必要とすることから、時間や労力がかかるという問題も抱えていた。
つまり、図１１に示す剛体モデルや、図１２（ａ）に示す２慣性モデルでは数値化が簡単だが、図１２（ｂ）の３慣性モデルではモデル化が難しくなり、図１２（ｃ）の１７慣性モデルでは、バネ・マスの組み合わせ構成の確定、バネ・マス・減衰各要素の分布値の確定、式の解法が困難である。
【００１２】
従来技術は、電動機制御系のサーボ調整に利用し、調整中に生ずる現象を観察し、人手でパラメータを調整して、対策を行うものであり、制御系が対象とする機械の特性を数値化するような推定をしていなかった。
２慣性以上の共振現象を伴っていても、機械振動検出装置の次数は常に等価剛体モデルの次数＋１ではあるが、現象を観察しながら、人手でパラメータを調整する形態だからこそ対応可能のことであり、機械共振のパラメータを設定しないので、多数の共振を持つ、制御対象の機械を明確に多慣性に数値モデル化できるものではない。
そこで、本発明はこのような問題点に鑑みてなされたものであり、周波数特性の計測値から自動的に反共振周波数や共振周波数、減衰を読み取るとともに多慣性の数値モデルを推定し、数値化し、容易にシミュレーションやサーボ調整に利用するための多慣性機械モデルを推定することができる電動機制御装置の多慣性機械モデル推定装置を提供することを目的とする。
【００１３】
【課題を解決するための手段】
上記問題を解決するため、本発明は次のように構成したことを特徴としている。
請求項１に記載の電動機制御装置の多慣性機械モデル推定装置の発明は、制御指令信号を作成する指令器と、前記指令器の作成した制御指令信号を受けて電動機を駆動する制御器と、前記電動機によって駆動される機械の動作量を検出する検出手段と、を有する電動機制御装置に、前記検出手段が検出した動作量と前記指令器が作成した制御指令信号を演算し計測して周波数特性を得る周波数特性演算装置を備えて成る電動機制御装置の多慣性機械モデル推定装置において、前記周波数特性演算装置が、予め入力される剛体モデルと複数の１自由度の振動モデルと高次周波数の影響度のモデルのいずれか１つ以上からなる周波数特性式を格納する記憶部と、前記周波数特性の共振周波数と反共振周波数からなる突起形状を自動的に算出する周波数特性ピーク検出部と、前記周波数特性ピーク検出部で検出した複数の共振周波数あるいは反共振周波数から減衰を推定する減衰推定値解析部と、前記記憶部に格納された前記剛体モデルと複数の１自由度の振動モデルと高次周波数の影響度のモデルのいずれか１つ以上からなる周波数特性式から算出されたＮ慣性モデル（Ｎは２以上の整数）の周波数特性に対して、前記計測して得られた周波数特性との誤差をそれぞれ算出する周波数特性誤差算出部と、を備えたことを特徴とする。
また、請求項２記載の発明は、請求項１記載の多慣性機械モデル推定装置において、前記周波数特性誤差算出部において得られた２〜Ｎの各慣性モデルの周波数特性の計算値と計測値との最小誤差と、前記剛体モデルの周波数特性の計算値と計測値の最小誤差とを比較し、何れか一方のモデルの誤差が少ない方を実際のモデルと判定するようにした機械モデル判定部を備えたことを特徴とする。
このようになっているため、周波数特性を計測することができ、また、計測した周波数特性計測値から自動的に反共振周波数や共振周波数、減衰を読み取ることにより、高価な計測装置を用いることなく、作業者が高度な専門知識や経験を持たなくても、容易にシミュレーションや電動機制御装置のサーボ調整に利用するための機械モデルを多慣性モデルとして推定することができる。
請求項３記載の発明は、請求項１または２記載の制御装置の多慣性機械モデル推定装置において、前記検出手段が、前記電動機の位置、速度若しくは加速度、または前記機械の位置、速度若しくは加速度を検出して前記動作量とすることを特徴とする。
このようになっているため、前記電動機の位置または速度または加速度を検出する回転検出器による検出手段や、機械に振動検出器による検出手段を用いても、また、さまざまな検出手段や制御系を選択して構成でき、高価な計測装置を用いることなく、作業者が高度な専門知識や経験を持たなくても、周波数特性を計測して、容易にシミュレーションや電動機制御装置のサーボ調整に利用するための機械モデルを多慣性モデルとして推定することができる。
【００１４】
請求項４記載の発明は、請求項１〜３のいずれか１項記載の電動機制御装置の多慣性機械モデル推定装置において、前記周波数特性誤差算出部が、前記周波数特性式に、前記動作指令信号と前記検出手段の信号から求める計測した周波数特性を曲線適合することにより、周波数特性の計算値と計測値の誤差を算出するようにしたことを特徴とする。
このようになっているため、周波数特性を曲線適合することにより、周波数特性の計算値と計測値との誤差を算出するようにした構成にしたので、計測した周波数特性と剛体モデルと複数の１自由度のモデルからなる式と比較することができ、高価な計測装置を用いることなく、作業者が高度な専門知識や経験を持たなくても、周波数特性を計測して、容易にシミュレーションや電動機制御装置のサーボ調整に利用するための機械モデルを多慣性モデルとして推定することができる。
また、周波数特性の計算値と計測値の誤差を算出でき、比較による剛体系と多慣性系の区別や、多慣性系のモデル次数を決定できる
しかも安価な電動機制御装置の多慣性機械モデル推定装置を提供することが可能となる。
【００１５】
請求項５記載の発明は、請求項１〜４のいずれか１項記載の電動機制御装置の多慣性機械モデル推定装置において、前記制御器が、前記検出手段の信号を（−）端子に入力する減算器と、その減算器の信号を受けて働き制御指令を出力するような、少なくとも１つの閉ループを備え、単位系にあわせて前記制御指令とそれぞれの成分の前記動作量を一致するように前記電動機を制御し、加算器が少なくとも１つの閉ループの中に設けられ、前記加算器に前記指令発生装置の信号を入力することで、計測した周波数特性を得ることを特徴とする。
このようになっているため、閉ループを組んだまま、周波数特性を計測でき、垂直軸を構成する場合に、計測中に機械の可動部の落下や、位置ずれを防ぎ、さらに機械の可動部がずれることで、機械特性が変化して共振周波数がずれて周波数特性の計測精度が低下する問題を防ぐことができ、高価な計測装置を用いることなく、作業者が高度な専門知識や経験を持たなくても、周波数特性を計測して、容易にシミュレーションや電動機制御装置のサーボ調整に利用するための機械モデルを多慣性モデルとして推定することができる。
【００１６】
請求項６記載の発明は、請求項１〜５のいずれか１項記載の電動機制御装置において、前記制御器が、少なくとも１つ以上の閉ループと、少なくとも１つ以上の前記閉ループを開閉するスイッチを備えることを特徴とする。
このようになっているため、周波数特性の計測において開ループと閉ループを切り替えられ、状況にあった使用方法を選択でき、高価な計測装置を用いることなく、作業者が高度な専門知識や経験を持たなくても、周波数特性を計測して、容易にシミュレーションや電動機制御装置のサーボ調整に利用するための機械モデルを多慣性モデルとして推定することができる。
請求項７記載の発明は、請求項１〜６のいずれか１項記載の電動機制御装置の多慣性機械モデル推定装置において、前記電動機制御装置を少なくとも１つ以上有し、少なくとも１つの前記指令器を制御する指令制御器とを備え、少なくとも１つの前記電動機を動作し、少なくとも１つ以上の前記検出手段が少なくとも１つ以上の機械の動作量を検出し、前記周波数特性演算装置が、少なくとも１つ以上の計測した周波数特性を得ることを特徴とする。
このようになっているため、他軸の検出手段の信号を利用した周波数特性が計測でき、軸間の干渉が評価でき、計測した周波数特性から多慣性モデルを推定でき、機械を多軸構成としてモデル化できる。高価な計測装置を用いることなく、作業者が高度な専門知識や経験を持たなくても、周波数特性を計測して、容易にシミュレーションや電動機制御装置のサーボ調整に利用するために多軸かつ多慣性の機械モデルを推定することができる。
請求項８記載の発明は、請求項１〜７のいずれか１項記載の電動機制御装置の多慣性機械モデル推定装置において、前記制御器が、駆動力を発生するアンプと、フィルタ手段を備え、前記制御指令信号を前記フィルタ手段の前から挿入し、周波数特性を計測することを特徴とする。
このようになっているため、周波数特性を疑次的にフィルタ手段により変化させて計測でき、機械の特性を変えたものとして、モデル化できる。また、フィルタ手段の効果を含んだ周波数特性を計測できるので、その効果を評価できる。
【００１７】
請求項９記載の発明は、請求項８記載の電動機制御装置の多慣性機械モデル推定装置において、前記制御器がさらに加算器を備えることを特徴とする。
このようになっているため、フィルタ手段の利用を簡易に切り替えられる。また、フィルタ手段の効果を含んだ周波数特性を計測できるので、その効果を評価することができる。
請求項１０記載の発明は、請求項１〜７のいずれか１項記載の電動機制御装置の多慣性機械モデル推定装置において、前記制御器が、前記検出手段による位置制限管理手段を備え、予め入力される位置制限値に基づき、周波数特性の計測中に前記機械の動作量を制限して稼働することを特徴とする。
このようになっているため、動作中に予め決められた動作量を超える危険なく、安全に周波数特性を計測できる。
請求項１１記載の発明は、請求項１〜７のいずれか１項に記載の電動機制御装置の多慣性機械モデル推定装置において、前記周波数特性演算装置が、表示装置、入力装置、および、記憶装置のいずれか１つまたはそれ以上を備えたことを特徴とする。
このようになっているため、計測した周波数特性や、多慣性化したモデルの結果を観察・確認でき、予め入力される周波数特性式や位置制限値の変更や、各種スイッチの変更が容易に実施でき、計測した周波数特性や多慣性化したモデルの結果を保存できることとなる。
【００１８】
【発明の実施の形態】
（第１実施の形態）
以下、本発明の具体的な実施の形態を図１から図８に基づいて説明する。
図１は、本発明の実施の形態を示す電動機制御装置の多慣性機械モデル推定装置の全体構成図である。
図１において、１は指令制御器、２は指令器、３は制御器、４は電動機、５は検出手段、６は機械、７は機械の可動部、１０は周波数特性演算装置、１１は周波数特性式となっている。
また、対象のマシンは２軸のＸＹ構成であり、Ｘ軸は電動機４の動作量を検出する検出手段５で電動機４を制御するセミクローズド制御系、Ｙ軸は電動機４の動作量を検出する検出手段５と機械の稼動部７の動作量を検出する検出手段５の両方を備えたフルクローズド制御系となっている。２軸構成と、制御系の違いにより、Ｘ軸には、指令器２ａ、制御器３ａ、電動機４ａ、検出手段５ａを備え、Ｙ軸には、指令器２ｂ、制御器３ｂ、電動機４ｂ、検出手段５ｂと検出手段５ｃを備えている。
図２は図１の構成図をブロック図に示したものである。図１との主な表現上の差異は電動機４および機械６である。ほかに、３１ａはＸ軸の位置制御器、３２ａはＸ軸の速度制御器、３１ｂはＹ軸の位置制御器、３２ｂはＸ軸の速度制御器、４０はスイッチである。
図１と図２は速度と位置の閉ループを構成しているが、スイッチ４０により開ループにしている。
電動機４および機械６は、電動機４と検出手段５の数と位置を考慮して、多入力多出力として、各要素Ｈｉ，ｊから構成し、周波数特性演算装置は、この各要素Ｈｉ，ｊを周波数特性として計測する。
なお、機械の各要素Ｈｉ，ｊについては動作の説明の中で詳述する。
【００１９】
図３は周波数特性演算装置の演算操作手順のうち、周波数特性の計測に関するフローチャートを示したものである。
図３において、１０Ａは周波数特性計測部、１０Ｂは周波数特性ピーク検出部、１０Ｃは減衰推定値解析部、１０Ｄは周波数特性誤差算出部、１０Ｅは機械モデル判定部である。
図４は本発明の実施の形態の周波数特性の計測から、計測した周波数特性値に基づいて機械モデルを判定する操作に関するフローチャートである。
図４のフローチャートに従い動作について説明する。
周波数特性演算装置１の演算操作手順については、周波数特性を計測するステップＳＴ１からステップＳＴ５の各ステップと、周波数特性式に基づいたモデルと計測した周波数特性を比較して、機械の特性をモデル化するステップＳＴ６からステップＳＴ１１の各ステップがある。
なお、各ステップのうち、ステップＳＴ４，５の処理は、図３で示した周波数特性演算装置１０の周波数特性計測部１０Ａに対応し、ステップＳＴ６、７の処理は周波数特性ピーク検出部１０Ｂに対応している。また、ステップＳＴ８の処理は減衰推定値解析部１０Ｃに対応すると共に、ステップＳＴ９，１０の処理は周波数特性誤差算出部１０Ｄに対応し、ステップＳＴ１１の処理は機械モデル判定部１０Ｅに対応している。
なお、ステップＳＴ２ｂ、ＳＴ２ｃ、およびステップＳＴ３ｂ、ＳＴ３ｃは第２の実施の形態で説明する。
最初に、ステップＳＴ１〜ステップＳＴ５の周波数特性の計測について説明する。
まず、ステップＳＴ１では、指令器２は動作指令信号を作成する。なお、多軸の場合には指令制御器１が、複数の指令器２を制御するものとする。
次に、ステップＳＴ２では、指令器２で出力された動作指令信号を制御機３に転送する。これにより、動作指令信号と等価な制御信号を電動機４に送り、ステップＳＴ２ａで該電動機４が動作することで、可動部７が動作し、振動を発生する。
そして、ステップＳＴ３では、検出手段５が電動機４の動作信号を検出し、制御機３を経由して検出手段信号を周波数特性演算装置１０に転送する。
動作指令信号が完了すれば、ステップＳＴ３ａで、電動機の動作を完了する。それから、ステップＳＴ４では、周波数特性演算装置１０で、動作指令信号と検出手段信号を例えばＦＦＴ演算し、周波数分析を行う。
ステップＳＴ５では、周波数特性演算装置１０において周波数分析した動作指令信号と検出手段信号から周波数特性を算出する。ここまでの処理により、周波数特性の計測が完了する。
【００２０】
本実施の形態は、２軸マシン構成なので、複数の周波数特性を機械要素Ｈｉ，ｊとして得ることができる。
図２のブロック図に示した機械要素Ｈｉ，ｊについて説明する。
２軸マシンでは、Ｘ軸の制御器３ａが電動機４ａに駆動力Ｔ１を与えると、可動部７を含む機械６が応答する。機械の特性により、Ｘ軸の検出手段５ａだけでなく、Ｙ軸の検出手段５ｂと検出手段５ｃにも応答を返す。また、同様にＹ軸の制御器ｂが電動機４ｂに駆動力Ｔ２を与えると、機械の特性により、Ｙ軸の検出手段５ｂと検出手段５ｃだけでなく、Ｘ軸の検出手段５ａにも応答を返す。
機械６の要素モＨｉ，ｊに、ある駆動力Ｔｉを与えた時の、応答Ｒｊは式（１３）のように
Ｒｊ＝Ｈｉ，ｊ・Ｔｉ 式（１３）
となるので、
Ｘ軸の電動機４ａからＸ軸の検出手段５ａまでの特性を持つ機械要素Ｈ_ＥＸ，Ｘ、
Ｘ軸の電動機４ａからＹ軸の検出手段５ｂまでの特性を持つ機械要素Ｈ_ＥＹ，Ｘ、
Ｘ軸の電動機４ａからＹ軸の検出手段５ｃまでの特性を持つ機械要素Ｈ_ＬＹ，Ｘ、
Ｙ軸の電動機４ｂからＸ軸の検出手段５ａまでの特性を持つ機械要素Ｈ_ＥＸ，Ｙ、
Ｙ軸の電動機４ｂからＹ軸の検出手段５ｂまでの特性を持つ機械要素Ｈ_ＥＹ，Ｙ、
Ｙ軸の電動機４ｂからＹ軸の検出手段５ｃまでの特性を持つ機械要素Ｈ_ＬＹ，Ｙを利用すれば、Ｘ軸の駆動力Ｔ１が与えられた、Ｘ軸の検出手段５ａの応答Ｒ_Ｅｘ，１の機械要素Ｈ_ＥＸ，Ｘとの関係は、
Ｒ_Ｅｘ，１＝Ｈ_ＥＸ，Ｘ・Ｔ_１ 式（１３．１） になり、
Ｘ軸の駆動力Ｔ１が与えられた、Ｙ軸の検出手段５ｂの応答Ｒ_ＥＹ，１の機械要素Ｈ_ＥＹ，Ｘとの関係は、
Ｒ_ＥＹ，１＝Ｈ_ＥＹ，Ｘ・Ｔ_１ 式（１３．２） になり、
Ｘ軸の駆動力Ｔ１が与えられた、Ｙ軸の検出手段５ｃの応答Ｒ_ＬＹ，１の機械要素Ｈ_ＬＹ，Ｘとの関係は、
Ｒ_ＬＹ，１＝Ｈ_ＬＹ，Ｘ・Ｔ_１ 式（１３．３） になり、
Ｙ軸の駆動力Ｔ２が与えられた、Ｘ軸の検出手段５ａの応答Ｒ_Ｅｘ，２の機械要素Ｈ_ＥＸ，Ｙとの関係は、
Ｒ_Ｅｘ，２＝Ｈ_ＥＸ，Ｙ・Ｔ_２ 式（１３．４） になり、
Ｙ軸の駆動力Ｔ２が与えられた、Ｙ軸の検出手段５ｂの応答Ｒ_ＥＹ，２の機械要素Ｈ_ＥＹ，Ｙとの関係は、
Ｒ_ＥＹ，２＝Ｈ_ＥＹ，Ｙ・Ｔ_２ 式（１３．５） になり、
Ｙ軸の駆動力Ｔ２が与えられた、Ｙ軸の検出手段５ｃの応答Ｒ_ＬＹ，２の機械要素Ｈ_ＬＹ，Ｙとの関係は、
Ｒ_ＬＹ，２＝Ｈ_ＬＹ，Ｙ・Ｔ_２ 式（１３．６） と表現できる。
【００２１】
Ｘ軸の電動機４ａとＹ軸の電動機４ｂを同時に動作させる場合を想定して、これらを検出手段５ごとに集めると、
Ｘ軸の検出手段５ａが検出する信号Ｒ１は
Ｒ１＝Ｒ_Ｅｘ，１＋Ｒ_Ｅｘ，２ 式（１４．１） となり、
Ｙ軸の検出手段５ｂが検出する信号Ｒ２は
Ｒ２＝Ｒ_ＥＹ，１＋Ｒ_ＥＹ，２ 式（１４．２） となり、
Ｙ軸の検出手段５ｃが検出する信号Ｒ３は
Ｒ３＝Ｒ_ＬＹ，１＋Ｒ_ＬＹ，２ 式（１４．３） となる。
なお、機械６には、電動機４の可動子を機械に含めた要素にする。
つまり、式（１４）は電動機４を含む機械６の要素Ｈ_ｉ，ｊと、駆動力Ｔ_ｊと検出手段５の信号Ｒ_ｉの関係を示しており、要素Ｈ_ｉ，ｊは周波数特性として計測できる。
【数６】

【００２２】
機械の各要素Ｈ_ｉ，ｊの関係は、以上の通りであり、本発明の実施の形態では、６つの周波数特性を得ることができる。
実際に機械の各要素Ｈ_ｉ，ｊつまり６つの周波数特性を計測するには、大きく２種類の手順があげられる。
・ Ｘ軸の電動機４ａとＹ軸の電動機４ｂを同時に動作させる場合
・ Ｘ軸の電動機４ａとＹ軸の電動機４ｂを単軸づつ動作させる場合
がある。
（１）Ｘ軸の電動機４ａとＹ軸の電動機４ｂを同時に動作させる場合
前記式（１４）から機械の各要素Ｈｉ，ｊを求めるには、
Ｘ軸とＹ軸の駆動力（トルク）から成る列ベクトル［Ｔ］の随伴（複素共役かつ転置）行列［Ｔ］^ｈを前記式（１４）の両辺の右側から掛け、
【数７】

【００２３】
・ Ｘ軸の電動機４ａとＹ軸の電動機４ｂを単軸づつ動作させる場合
単軸ごとに電動機４を駆動し、他方の電動機４は駆動しない場合である。
前記式（１４）からＸ軸のみ電動機４ａを駆動する場合には、式（１４ａ）となり、
【数８】

となるので、機械の各要素Ｈｉ，ｊの周波数特性を計測できる。
なお、ステップＳＴ３では、制御機３が動作指令信号と検出信号を保持しているので、検出手段５が電動機４の動作信号を検出し、制御機３を経由して検出手段信号を周波数特性演算装置１０に転送するとしたが、指令器２と検出手段５から直接、周波数特性演算装置１０へ信号を転送しても良い。
【００２４】
次に、ステップＳＴ６〜ステップＳＴ１１の機械特性のモデル化について説明する。機械要素つまり計測した周波数特性Ｈ_ＥＸ，Ｘ、Ｈ_ＥＹ，Ｘ、Ｈ_ＬＹ，Ｘ、Ｈ_ＥＸ，Ｙ、Ｈ_ＥＹ，Ｙ、Ｈ_ＬＹ，ＹそれぞれをステップＳＴ６〜ステップＳＴ１１に適用できる。
まず、機械特性のモデルは、剛体モデルと多慣性モデルに分けることができる。
補足のため、図１１、図１２を使って剛体モデルと多慣性モデルの説明をする。
図１１は、剛体モデルを示した図である。
すなわち、図１に示した電動機４、機械６、可動部７を単純な剛体負荷に近似したものとなっている。
また、図１２（ａ）は２慣性モデルの概要を示した図である。
すなわち、図１に示した電動機４、機械６、可動部７を、電動機側負荷（Ｊ１）と負荷側負荷（Ｊ２）からなる２つのマスと、２つのマス、２つの負荷を結ぶバネ（Ｋ：バネ定数）および減衰（Ｄ：減衰定数）とより２慣性モデルに近似したものとなっている。
図１２（ｂ）は３慣性モデルを示した図である。ここで、図１２（ｂ）の（ｉ）は図１に示した電動機４、機械６、可動部７を、３つの負荷（Ｊ１）と、（Ｊ２）と、（Ｊ３）からなる３つのマスと、３つのマス、３つの負荷を結ぶバネ（Ｋ：バネ定数）および減衰（Ｄ：減衰定数）とより３慣性モデルに近似したものとなっている。また、図１２（ｂ）の（ｉｉ）は電動機４、機械６、可動部７を、電動機側負荷（Ｊ１）と負荷側負荷（Ｊ２）からなる２慣性モデルと、負荷側負荷の反力を受ける機械部のベースとなる負荷（Ｊ３） から成り、計３つのマスと、計３つのバネ（Ｋ：バネ定数）および減衰（Ｄ：減衰定数）とより３慣性モデルに近似したものとなっている。
さらに、図１２（ｃ）は電動機４、機械６、可動部７を、計１７個のマスと、計１８個のバネ（Ｋ：バネ定数）および減衰（Ｄ：減衰定数）と、負荷の反力を受ける関係を示した１７慣性モデルにした例である。
このうち、図１１に示した剛体モデルにおいて、動作指令信号から検出手段５の速度信号までの周波数特性Ｈ_ｒのモデル式は式（１６）となる。
【数９】

しかしながら、モデルの次数が大きくなると、バネ・マスの組み合わせや、各負荷の値（慣性モーメントや重量）の分布や、周波数特性Ｈ_ｒのモデル式が複雑になる。
【００２５】
図５は本発明の実施の形態における１自由度モデルを示した図である。
多慣性の機械によって発生する振動は、図５のような１自由度系の振動が複数集まっていると考えて、１自由度系の総和が周波数特性Ｈ_ｒのモデル式となるように、周波数特性式を構成する。共振周波数ごとに１自由度系の振幅と位相を調整し、計測した周波数特性にあわせて、１自由度系のモデルの総和とすればよい。機械特性を比例粘性減衰とすれば、速度応答では、周波数特性式は式（１８）となる。
【数１０】

ここで、
Ｈ_ｉ，ｊ：周波数特性、Ｒ^（ｖ） _ｊ：検出手段５の速度信号，Ｔ_ｉ：駆動力、ζ_ｒ：ｒ次の減衰比、ω_ｒ： ｒ次の固有振動数、Ａ_ｒ：ｒ次の留数、
Ｊ：剛体負荷の慣性モーメントもしくは質量、
Ｎ： 固有振動数（共振）の数、ｓ：ラプラス演算子
動作量の単位が変わり、位置（変位）応答であれば 周波数特性式は式（１９）になる。
【数１１】

ここで、Ｒ^（ａ） _ｊ：検出手段５の加速度信号
モデル化の対象となる周波数領域より高い周波数の影響Ｂを考慮して
周波数特性式を以下の式（２１）〜（２３）としてもよい。
【数１２】

【００２６】
また、一般構造粘性減衰として、高次周波数の影響度Ｂを考慮して、周波数特性式を、以下の式（２４）〜（２６）にしても良いし、Ｂの項を省略してもよい。
【数１３】

この高次周波数の影響度（剰余剛性）Ｂは図２の周波数特性式の１つとしてメモリ１１の中に格納されることができる。したがって、メモリ１１の周波数特性式には、（ｉ）剛体モデル、（ｉｉ）複数の１自由度の振動モデル、（ｉｉｉ）高次周波数の影響度のモデルが考えられ、使用する周波数特性式はこれらのうち少なくとも１つから成っている。
なお、機械の可動部７の影響が大きい場合には 剛体系の影響１／（Ｊ・ｓ）を必ず適用するが、検出手段の位置が駆動力との関係から、機械の可動部７の影響が無い場合には剛体系の影響１／（Ｊ・ｓ）を省略してよい。
【００２７】
また、ステップＳＴ６では、多慣性とする数は、共振の数と関係があるので、計測した周波数特性から、山側のピーク、谷側のピークを算出する。
ピーク算出の方法は公知の複素スペクトル内挿法や平滑化微分法などの方法を使用すればよい。
ステップＳＴ７では、ステップＳＴ６で検出したピークがあるかを判断する。
山側のピーク、谷側のピークを検出できなければ、剛体と判定できる。
剛体モデルへの近似のためには、剛体負荷もしくは電動機側負荷４と機械可動部を含む各軸の負荷の和Ｊがわかれば良い。
なお、剛体モデルは前記式（１６）なので、周波数特性つまり機械要素Ｈの形状から剛体負荷Ｊを求められる。剛体モデルの周波数特性をグラフ化すると、右下がりのゲイン特性となる。
ステップＳＴ７で、ピークが在ると判定されれば、ステップＳＴ８とステップＳＴ１０の処理を行う。
ステップＳＴ８では、検出したピークの周波数を用いることにより、公知の減衰推定法に基づいて減衰を推定することができる。なお、ピークがある場合にも機械の可動部７の影響が大きい場合には、剛体系の特徴が計測した周波数特性の低周波数域に現れるので、剛体系の負荷成分Ｊが未確定ならば、谷側のピークより低い低周波数領域に制限した範囲において、計測した周波数特性に、前記式（１６）を適用し、最小二乗法により剛体系の負荷成分Ｊを算出してもよい。
そして、ステップＳＴ９では、仮設定したピークを、周波数特性演算装置１０に予め入力される周波数特性式、つまり式（１８）〜（２６）のいずれかにより、ピークの数だけ、もしくは選択したピークの数だけにより曲線適合し、式（１８）〜（２６）のいずれかと計測値との誤差を算出する。
【００２８】
負荷成分Ｊと、山側のピーク、谷側のピーク、つまり共振および反共振周波数、減衰がわかっているので、例えば、式（１８）の分母の項はすべてわかっているので、留数Ａｒの正負である位相と大きさである振幅を計測値に合うよう決定すれば良い。
周波数特性演算装置１０に入力される周波数特性式のひとつである式（１８）〜（２６）のいずれかと、共振周波数ごとに、各値を決定し、これを足し合わせることで、曲線適合できる。
ステップＳＴ１０は、ステップＳＴ８、ＳＴ９と平行して処理するステップであり、ピークが検出された場合でも、周波数特性演算装置１０に入力される周波数特性式のひとつである式（１６）により剛体モデルに曲線適合した結果と計測した周波数特性との誤差を算出しておく。
次に、ステップＳＴ１１で、多慣性モデルの誤差と、剛体モデルの誤差を比較し、剛体モデルの誤差が少ない場合には、剛体モデルと判定できる。
多慣性モデルの誤差が少ない場合には、多慣性モデルと判定できる。つまり、式（１８）〜（２６）のいずれかと計測した周波数特性との誤差と、式（１６）と計測した周波数特性との誤差を比較して、剛体系の式（１６）と多慣性の式（１８）〜（２６）のいずれかへのモデル化で、どちらの誤差が小さく、モデルに最適かを判断することができる。
【００２９】
図６は本発明の実施の形態における計測した周波数特性の一例を示すグラフ図である。
図７は本発明の実施の形態における曲線適合して多慣性モデルにした周波数特性の一例を示すグラフ図である。
図８は本発明の実施の形態における多慣性モデルにした周波数特性の一例のブロック図である。
図９と図１０は、計測した周波数特性から、複数の計測した周波数特性を考慮して、剛体モデルと１６個の固有振動数（共振）をピークから判定し、その減衰を推定し、前記式（１８）により、曲線適合した例である。このように高次の多慣性モデルを推定できている。
計測した周波数特性の周波数の範囲を広げれば、高次の共振周波数と減衰と前記式（１８）により、多慣性モデルをさらに高次化できる。
モデルの判定が完了すれば、周波数特性演算装置１０に接続した出力装置２１に結果を出力でき、シミュレーションや電動機制御装置の調整に利用できる。また記憶装置２３に結果を保存することもできる。
入力装置２２は、予め入力しておく周波数特性式の変更や、その他の操作を実施できる。
計測した周波数特性Ｈ_ＥＸ，Ｘ、Ｈ_ＥＹ，Ｘ、Ｈ_ＬＹ，Ｘ、Ｈ_ＥＸ，Ｙ、Ｈ_ＥＹ，Ｙ、Ｈ_ＬＹ，Ｙのそれぞれをモデル化すれば、図２のブロック図の機械要素の数値化が可能となり、多軸構成のシミュレーションや電動機制御装置の調整に利用できる。
【００３０】
（第２実施の形態）
次に、本発明の第２の実施の形態を図９に基づいて説明する。
図９は第２の実施の形態の構成を示すブロック図である。
図９において、１は指令制御器、２は指令器、３は制御器、４は電動機、５は検出手段、６は機械、７は機械の可動部、１０は周波数特性演算装置、１１は周波数特性式、３４は加算器、３５はアンプ、３６はフィルタ手段、３８は位置管理手段、３９は位置制限管理手段、４０はスイッチとなっている。
このように、１軸の開ループ構成をしており、第１の実施の形態と同様に図４のフローチャートのステップＳＴ１からＳＴ５に従い周波数特性を計測できる。第１の実施の形態との違いは、位置管理手段３８と位置制限管理手段３９を有しているので、周波数特性の計測において、ステップＳＴ２ａで電動機４が動作し、ステップＳＴ３ａの電動機の動作が完了する間に、動作検出量が、予め入力しておく位置制限値Ｌを超えた場合をステップＳＴ２ｂにて判定し、ステップＳＴ２ｃにて、位置制限管理手段３９を動作し、例えばアンプ３５からの駆動力を遮断して、電動機を停止することができる。動作量が大きい場合に、可動部７が機械の他部位と衝突するような危険を回避することが可能となる。
また、スイッチ４０にて、駆動力Ｔをフィルタ手段３６を通して、電動機４や機械６に与えるように切り替えれば、フィルタ手段３６の特性を含む周波数特性を計測することができる。
以下ステップＳＴ６からステップＳＴ１１までは、第１の実施の形態と同様に多慣性モデルを推定でき、モデルの判定が完了すれば、周波数特性演算装置１０に接続した出力装置２１に結果を出力でき、シミュレーションや電動機制御装置の調整に利用できる。また記憶装置２３に結果を保存することもできる。
入力装置２２は、予め入力しておく周波数特性式や位置制限値Ｌの変更や、その他の操作を実施できる。
【００３１】
（第３実施の形態）
さらに、本発明の第３の実施の形態を図１０に基づいて説明する。
図１０は第３実施の形態の構成を示すブロック図である。
図１０において、１は指令制御器、２は指令器、３は制御器、４は電動機、５は検出手段、６は機械、７は機械の可動部、１０は周波数特性演算装置、１１は周波数特性式、３１は位置制御器、３２は速度制御器、３３は減算器、３４は加算器、３５はアンプ、３６はフィルタ手段、３７は微分機、４０はスイッチとなっている。
図１０は１軸の閉ループ構成となっており、第２の実施の形態である図９との違いは、位置制御器３１と速度制御器３２および微分機３７からなる２つの閉ループを持ち、位置指令Ｃ＝０とし、２つの閉ループの内部の加算器３４に、指令器２からの動作指令信号を入力している。
このように成っているので、検出手段５の信号と、閉ループを巡回した成分を含むアンプ３５から出力された駆動力信号と指令器２からの動作指令信号の和を周波数特性演算装置１０に与えている。
また、位置ループを組んで、位置指令Ｃ＝０を与えているので、位置管理手段３８、位置制限管理手段３９を備えていない。
第３の実施の形態の動作は第１および第２の実施の形態と同様であり、前記図４のフローチャートに沿って動作する。図４のフローチャートのステップＳＴ１からＳＴ５に従い周波数特性を計測できる。また、スイッチ４０にて、駆動力Ｔをフィルタ手段３６を通して、電動機４や機械６に与えるように切り替えれば、第２の実施の形態と同様にフィルタ手段３６の特性を含む周波数特性を計測することができる。 さらに、第１および第２の実施の形態と同様に、ステップＳＴ６からＳＴ１１に従い、周波数特性から多慣性モデルを推定でき、モデルの判定が完了すれば、周波数特性演算装置１０に接続した出力装置２１に結果を出力でき、シミュレーションや電動機制御装置の調整に利用できる。
【００３２】
【発明の効果】
以上述べたように、請求項１に記載の電動機制御装置の多慣性機械モデル推定装置の発明によれば、制御指令信号を作成する指令器と、前記指令器の作成した制御指令信号を受けて電動機を駆動する制御器と、前記電動機によって駆動される機械の動作量を検出する検出手段と、を有する電動機制御装置に、前記検出手段が検出した動作量と前記指令器が作成した制御指令信号を演算し計測して周波数特性を得る周波数特性演算装置を備えて成る電動機制御装置の多慣性機械モデル推定装置において、前記周波数特性演算装置が、予め入力される剛体モデルと複数の１自由度の振動モデルと高次周波数の影響度のモデルのいずれか１つ以上からなる周波数特性式を格納する記憶部と、前記周波数特性の共振周波数と反共振周波数からなる突起形状を自動的に算出する周波数特性ピーク検出部と、前記周波数特性ピーク検出部で検出した複数の共振周波数あるいは反共振周波数から減衰を推定する減衰推定値解析部と、前記記憶部に格納された前記剛体モデルと複数の１自由度の振動モデルと高次周波数の影響度のモデルのいずれか１つ以上からなる周波数特性式から算出されたＮ慣性モデル（Ｎは２以上の整数）の周波数特性に対して、前記計測して得られた周波数特性との誤差をそれぞれ算出する周波数特性誤差算出部と、を備え、また、請求項２記載の発明によれば、請求項１記載の多慣性機械モデル推定装置において、前記周波数特性誤差算出部において得られた２〜Ｎの各慣性モデルの周波数特性の計算値と計測値との最小誤差と、前記剛体モデルの周波数特性の計算値と計測値の最小誤差とを比較し、何れか一方のモデルの誤差が少ない方を実際のモデルと判定するようにしたので、周波数特性を計測することができ、また、計測した周波数特性計測値から自動的に反共振周波数や共振周波数、減衰を読み取ることにより、高価な計測装置を用いることなく、作業者が高度な専門知識や経験を持たなくても、容易にシミュレーションや電動機制御装置のサーボ調整に利用するための機械モデルを多慣性モデルとして推定することができるようになる。
【００３３】
請求項３記載の発明によれば、請求項１または２記載の制御装置の多慣性機械モデル推定装置において、前記検出手段が、前記電動機の位置、速度若しくは加速度、または前記機械の位置、速度若しくは加速度を検出して前記動作量とするので、前記電動機の位置または速度または加速度を検出する回転検出器による検出手段や、機械に振動検出器による検出手段を用いても、また、さまざまな検出手段や制御系を選択して構成でき、高価な計測装置を用いることなく、作業者が高度な専門知識や経験を持たなくても、周波数特性を計測して、容易にシミュレーションや電動機制御装置のサーボ調整に利用するための機械モデルを多慣性モデルとして推定することができるようになる。
請求項４記載の発明によれば、請求項１〜３のいずれか１項記載の電動機制御装置の多慣性機械モデル推定装置において、前記周波数特性誤差算出部が、前記周波数特性式に、前記動作指令信号と前記検出手段の信号から求める計測した周波数特性を曲線適合することにより、周波数特性の計算値と計測値の誤差を算出するようにしたので、計測した周波数特性と剛体モデルと複数の１自由度のモデルからなる式と比較することができ、高価な計測装置を用いることなく、作業者が高度な専門知識や経験を持たなくても、周波数特性を計測して、容易にシミュレーションや電動機制御装置のサーボ調整に利用するための機械モデルを多慣性モデルとして推定することができるようになる。
また、周波数特性の計算値と計測値の誤差を算出でき、比較による剛体系と多慣性系の区別や、多慣性系のモデル次数を決定できるようになる
しかも安価な電動機制御装置の多慣性機械モデル推定装置を提供することが可能となる。
【００３４】
請求項５記載の発明によれば、請求項１〜４のいずれか１項記載の電動機制御装置の多慣性機械モデル推定装置において、前記制御器が、前記検出手段の信号を（−）端子に入力する減算器と、その減算器の信号を受けて働き制御指令を出力するような、少なくとも１つの閉ループを備え、単位系にあわせて前記制御指令とそれぞれの成分の前記動作量を一致するように前記電動機を制御し、加算器が少なくとも１つの閉ループの中に設けられ、前記加算器に前記指令発生装置の信号を入力することで、計測した周波数特性を得るようにしたので、閉ループを組んだまま、周波数特性を計測でき、垂直軸を構成する場合に、計測中に機械の可動部の落下や、位置ずれを防ぎ、さらに機械の可動部がずれることで、機械特性が変化して共振周波数がずれて周波数特性の計測精度が低下する問題を防ぐことができ、高価な計測装置を用いることなく、作業者が高度な専門知識や経験を持たなくても、周波数特性を計測して、容易にシミュレーションや電動機制御装置のサーボ調整に利用するための機械モデルを多慣性モデルとして推定することができるようになる。
請求項６記載の発明によれば、請求項１〜５のいずれか１項記載の電動機制御装置において、前記制御器が、少なくとも１つ以上の閉ループと、少なくとも１つ以上の前記閉ループを開閉するスイッチを備えるので、周波数特性の計測において開ループと閉ループを切り替えられ、状況にあった使用方法を選択でき、高価な計測装置を用いることなく、作業者が高度な専門知識や経験を持たなくても、周波数特性を計測して、容易にシミュレーションや電動機制御装置のサーボ調整に利用するための機械モデルを多慣性モデルとして推定することができるようになる。
請求項７記載の発明によれば、請求項１〜６のいずれか１項記載の電動機制御装置の多慣性機械モデル推定装置において、前記電動機制御装置を少なくとも１つ以上有し、少なくとも１つの前記指令器を制御する指令制御器とを備え、少なくとも１つの前記電動機を動作し、少なくとも１つ以上の前記検出手段が少なくとも１つ以上の機械の動作量を検出し、前記周波数特性演算装置が、少なくとも１つ以上の計測した周波数特性を得るようにしたので、他軸の検出手段の信号を利用した周波数特性が計測でき、軸間の干渉が評価でき、計測した周波数特性から多慣性モデルを推定でき、機械を多軸構成としてモデル化できる。また、高価な計測装置を用いることなく、作業者が高度な専門知識や経験を持たなくても、周波数特性を計測して、容易にシミュレーションや電動機制御装置のサーボ調整に利用するために多軸かつ多慣性の機械モデルを推定することができるようになる。
【００３５】
請求項８記載の発明によれば、請求項１〜７のいずれか１項記載の電動機制御装置の多慣性機械モデル推定装置において、前記制御器が、駆動力を発生するアンプと、フィルタ手段を備え、前記制御指令信号を前記フィルタ手段の前から挿入し、周波数特性を計測するので、周波数特性を疑次的にフィルタ手段により変化させて計測でき、機械の特性を変えたものとして、モデル化できる。また、フィルタ手段の効果を含んだ周波数特性を計測できるので、その効果を評価できる。
請求項９記載の発明によれば、請求項８記載の電動機制御装置の多慣性機械モデル推定装置において、前記制御器がさらに加算器を備えるようにしたので、フィルタ手段の利用を簡易に切り替えることができ、また、フィルタ手段の効果を含んだ周波数特性を計測できるので、その効果を評価することができるようになる。
請求項１０記載の発明によれば、請求項１〜７のいずれか１項記載の電動機制御装置の多慣性機械モデル推定装置において、前記制御器が、前記検出手段による位置制限管理手段を備え、予め入力される位置制限値に基づき、周波数特性の計測中に前記機械の動作量を制限して稼働するので、動作中に予め決められた動作量を超える危険なく、安全に周波数特性を計測できるようになる。
請求項１１記載の発明によれば、請求項１〜７のいずれか１項に記載の電動機制御装置の多慣性機械モデル推定装置において、前記周波数特性演算装置が、表示装置、入力装置、および、記憶装置のいずれか１つまたはそれ以上を備えるようにしたので、計測した周波数特性や、多慣性化したモデルの結果を観察・確認でき、予め入力される周波数特性式や位置制限値の変更や、各種スイッチの変更が容易に実施でき、計測した周波数特性や多慣性化したモデルの結果を保存できることとなる。
【図面の簡単な説明】
【図１】本発明の実施の形態を示す多慣性機械モデル推定装置を備えた電動機制御装置の全体構成図である。
【図２】図１の構成を示すブロック図である。
【図３】周波数特性演算装置の演算操作手順のうち、周波数特性の計測に関するフローチャートを示したものである。
【図４】本発明の実施の形態における周波数特性演算装置の演算操作手順のうち、計測した周波数特性値に基づいて多慣性機械モデルを判定する操作に関するフローチャートを示したものである。
【図５】本発明の実施の形態における１自由度モデルを示した図である。
【図６】本発明の実施の形態における計測した周波数特性の一例を示すグラフ図である。
【図７】本発明の実施の形態における曲線適合して多慣性モデルにした周波数特性の一例を示すグラフ図である。
【図８】本発明の実施の形態における多慣性モデルにした周波数特性の一例のブロック図である。
【図９】本発明の第２の実施の形態における多慣性機械モデル推定装置を備えた電動機制御装置の構成を示すブロック図である。
【図１０】本発明の第３の実施の形態における多慣性機械モデル推定装置を備えた電動機制御装置の構成を示すブロック図である。
【図１１】剛体モデルを示した図である。
【図１２】多慣性モデルを示した図である。
【図１３】従来技術の共振機構の一例を説明する説明図である。
【図１４】従来技術の原理と実施例を説明するブロック図である。
【図１５】従来技術の第２の実施例のブロック線図である。
【符号の説明】
１：指令制御器
２：指令器
２ａ： Ｘ軸の指令器
２ｂ：Ｙ軸の指令器
３：制御器
３ａ： Ｘ軸の制御器
３ｂ： Ｙ軸の制御器
４：電動機
４ａ： Ｘ軸の電動機
４ｂ： Ｙ軸の電動機
５：検出手段
５ａ： Ｘ軸の電動機の検出手段
５ｂ： Ｙ軸の電動機の検出手段，
５ｃ： Ｙ軸の機械動作量の検出手段
６：機械
７；機械の可動部
１０：周波数特性演算装置
１０Ａ：周波数特性計測部
１０Ｂ：周波数特性ピーク検出部
１０Ｃ：減衰推定値解析部
１０Ｄ：周波数特性誤差算出部
１０Ｅ：機械モデル判定部
１１：周波数特性式を格納する記憶部
２１：出力手段
２２：操作手段
２３：記憶装置[0001]
BACKGROUND OF THE INVENTION
The present invention relates to an electric motor control apparatus, for example, used in a positioning apparatus such as a semiconductor manufacturing apparatus or a machine tool or an industrial robot, and more particularly to a multi-inertia machine model estimation apparatus for an electric motor control apparatus for optimal servo adjustment. It is about.
[0002]
[Prior art]
2. Description of the Related Art Conventionally, for example, there are mechanical resonance detection devices and vibration suppression control devices shown in FIGS. 13 to 15 that are used for positioning devices such as semiconductor manufacturing devices and machine tools, or industrial robots. , See Patent Document 1).
[0003]
[Patent Document 1]
Japanese Patent Laid-Open No. 7-337057
[0004]
That is, the machine resonance detection device and the vibration suppression control device estimate the machine vibration at a high speed, easily perform gain adjustment and parameter setting in the field, and take out the machine resonance signal with high accuracy. The object of the present invention is to provide a vibration detection device and a vibration suppression control device. The mechanical vibration detection device is composed of equivalent rigid body model means, first compensation means, and second compensation means. The mechanical vibration detection device detects the mechanical resonance signal and the disturbance estimation signal, compensates the mechanical resonance signal with the third and fourth compensation means, and performs the vibration suppression control through the addition amplification means. ing. Hereinafter, the prior art will be described with reference to the drawings.
FIG. 13 is an explanatory diagram for explaining an example of a resonance mechanism according to the prior art. FIG. 14 is a block diagram for explaining the principle of the prior art and the mechanical vibration detection device of the embodiment. FIG. 15 is a block diagram illustrating a mechanical vibration detection device and a vibration suppression control device according to a second embodiment of the prior art. 14 and 15, reference numeral 400 denotes a mechanical vibration detection device, and reference numeral 406 denotes a mechanism system including an electric motor.
In the prior art, when the mechanism system including the motor as shown in FIG. 13 is expressed as a two-inertia system, the frequency characteristic from the torque generated by the motor to the angular velocity (observed output) of the motor is expressed by Equation (1).
[Expression 1]

Formula (1) combines the parameters of the electric motor and the parameters of the mechanical resonance system, and it is very difficult to factorize the formula into the product of the primary expression and the secondary expression. For this reason, the physical meaning of the equation is unclear, and a state observer must be configured for the entire control system.
[0005]
Therefore, in the prior art, an approximate expression of P (s) is obtained by Expression (3).
[Expression 2]

[0006]
[Equation 3]

[0007]
From equation (6), the two-inertia resonance system is an equivalent rigid system G shown in a broken-line block 406 in FIG.₁(S) and mechanical resonance system G₂(S) and can be separated. If the speed of the equivalent rigid system can be detected, only the mechanical vibration component can be detected from the difference signal obtained by subtracting the detected speed of the equivalent rigid system from the observed output.
The velocity of the equivalent rigid system is a virtual state quantity and cannot be detected. When a state observer is used, the velocity of the equivalent rigid system can be observed. However, simply by applying the design theory of the state observer, the observer becomes a mechanical resonance system G₂(S) must be included.
For this reason, in the prior art, focusing on the fact that the mechanical system could be separated into an equivalent rigid system and a resonant system, the equivalent rigid system has a physically low-pass characteristic and hardly responds to high-frequency resonance components. Thus, an equivalent rigid body observer (a state observer modeled on an equivalent rigid system) is constructed by approximating the angular velocity of the motor (resonant system output in FIG. 14) to the observed output.
When a variable proportional to the torque of the motor is input to u (t) and the angular velocity of the motor is input to y (t), the mechanical resonance signal

And the estimated disturbance signal

The structure which obtains can be comprised like Formula (8).
[0008]
[Expression 4]

The parameters that must be set in the prior art mechanical vibration detector are the state observer bandwidth ω_fAnd Q_f, And γ and D which mean the parameters of the mechanism including the motor₀It is. Parameter ω_fAnd Q_fCan be set as required.
The moment of inertia and viscous friction coefficient of the motor and load (transmission mechanism) are obtained by calculation or measurement.₀Or measuring the resonance frequency and anti-resonance frequency of the mechanical resonance system to calculate γ.
[0009]
An embodiment of the prior art will be described with reference to FIG.
Fig. 14 shows the estimated equivalent rigid body velocity.

Are equivalent rigid body model means, first compensation means for performing proportional calculation, and second compensation means for performing integral calculation.
In FIG. 14, a signal u (t) proportional to the motor torque (for example, a torque monitor signal or torque command signal of the motor torque control device) and an observation output signal that is the angular velocity of the motor are input to the mechanical vibration detection device 400. Then, the mechanical resonance signal and the estimated disturbance signal are output independently.
[0010]
A second embodiment of the prior art will be described with reference to FIG.
FIG. 15 shows a mechanical vibration detection apparatus 400 similar to that in FIG. 14, a third compensation means composed of an integral element or a first-order lag element, a fourth compensation means composed of an integral element or a first-order lag element, And an addition input unit comprising an oscillation feedback gain (Kf) unit, an inversion / non-inversion amplification unit, and an addition unit (indicated by an addition point in the figure).
The mechanical resonance signal estimated by the mechanical vibration detection device 400 is input to the third compensation means, the output of the third compensation means is input to the fourth compensation means, and the output is input to the addition amplification means. The adding means adds a mechanical resonance signal that has passed through the third and fourth compensating means and the inverting / non-inverting amplifying means and a torque command signal output from a higher-level control device (for example, a speed control compensator). And output as a torque command signal for the torque control device of the motor. A torque command signal of the motor torque control device or a torque monitor signal of the motor torque control device is input to the mechanical vibration detection device as a signal u (t) proportional to the motor torque.
If gain adjustment or the like is performed, the frequency band of the speed control system can be expanded while suppressing mechanical resonance.
As described above, the conventional technology theoretically separates the mechanical resonance system into an equivalent rigid system and a resonant system. The equivalent rigid system has a physically low-pass characteristic, and is almost responsive to high-frequency resonance components. Based on the idea of not performing, the angular velocity signal of the motor is constructed by approximating the angular velocity of the motor (the output of the resonance system in FIG. 14) with the observed output to form an equivalent rigid body observer that is a state observer modeled on the equivalent rigid system. In addition, the mechanical vibration component and the disturbance component are detected independently from the signal proportional to the torque of the motor, and the deterioration of the quality of the mechanical resonance signal due to the mixing of the disturbance component is prevented.
In addition, the conventional technology does not use a mechanical vibration system model for the equivalent rigid body observer, and the order of the mechanical vibration detection device is always the order of the equivalent rigid body model + 1 even if there is a resonance phenomenon of 2 inertia or more. The number of parameters to be increased and set is not increased, and the parameters of mechanical resonance that are difficult to measure are not set.
In addition, the equivalent rigid body observer is provided with a feedback loop composed of the first compensation means (proportional) and the second compensation means (integration) so that the detected mechanical vibration signal component does not easily change the parameters of the observer. Yes.
A high-frequency mechanical vibration is detected by the mechanical resonance detection apparatus of the prior art, and the vibration control device widens the speed control system by suppressing and adjusting the high-frequency mechanical vibration.
[0011]
[Problems to be solved by the invention]
However, in the prior art, the modeling of the mechanical system including the electric motor is configured by a concentrated mass, a spring, and a damping, and if the number of mass-springs increases, the number of combinations of springs and masses increases. There was a problem. In the two-inertia model, there is one combination of spring, mass, and damping, but when there are three or more inertias, there are two or more combinations. For this reason, there is a problem in that the number of combinations increases and the configuration cannot be determined when there is multiple inertia.
Even if the combined configuration of springs and masses can be determined, even if the total amount of moment of inertia or weight can be determined or estimated, a plurality of masses with multiple inertias, and the distribution values of spring stiffness and damping are unknown.
Furthermore, since the equation becomes complicated when the order of the multi-inertia model becomes large, there is a problem that a high-order model equation cannot be defined.
Frequency characteristics H from the torque generated by the motor to the angular velocity (observed output) of the motor in accordance with the prior art_{i, j}Is converted into multi-inertia and converted into a numerical value, the result is as shown in Equation (12)
[Equation 5]

Each coefficient a₀, ... a_m, B₀, ..., b_2nBecause it requires advanced expertise and experience, it also takes time and effort.
That is, the rigid body model shown in FIG. 11 and the two-inertia model shown in FIG. 12A are easy to digitize, but the three-inertia model shown in FIG. In the inertial model, it is difficult to determine the spring / mass combination configuration, to determine the distribution values of the spring / mass / damping elements, and to solve the equations.
[0012]
The conventional technology is used for servo adjustment of the motor control system, observes the phenomenon that occurs during the adjustment, adjusts the parameters manually, and takes countermeasures, and the characteristics of the machine targeted by the control system are quantified I did not make an estimate.
Even when there is a resonance phenomenon of 2 inertia or more, the order of the mechanical vibration detection device is always the order of the equivalent rigid body model +1, but it is possible to cope with it because the parameter is adjusted manually while observing the phenomenon. Since the machine resonance parameters are not set, it is not possible to make a numerical model of a controlled machine having a large number of resonances clearly and multi-inertia.
Therefore, the present invention has been made in view of such problems, and automatically reads the anti-resonance frequency, resonance frequency, and attenuation from the measured value of the frequency characteristic and estimates a numerical model of multi-inertia to quantify it. An object of the present invention is to provide a multi-inertia machine model estimation device for an electric motor control device that can easily estimate a multi-inertia machine model for use in simulation and servo adjustment.
[0013]
[Means for Solving the Problems]
In order to solve the above problems, the present invention is characterized by the following configuration.
The invention of the multi-inertia machine model estimation device for an electric motor control device according to claim 1, a command device that creates a control command signal, a controller that drives the motor in response to the control command signal created by the command device, A motor control device having a detection means for detecting an operation amount of a machine driven by the electric motor, and calculating and measuring an operation amount detected by the detection means and a control command signal created by the command device to obtain a frequency characteristic. In the multi-inertia machine model estimation device for an electric motor control device comprising a frequency characteristic calculation device for obtaining a frequency characteristic, the frequency characteristic calculation device includes a rigid body model, a plurality of one-degree-of-freedom vibration models, and an influence of a higher-order frequency. A storage unit for storing a frequency characteristic formula comprising any one or more of the degree models, and a loop for automatically calculating a protrusion shape comprising a resonance frequency and an anti-resonance frequency of the frequency characteristic. A number characteristic peak detection unit, an attenuation estimation value analysis unit that estimates attenuation from a plurality of resonance frequencies or antiresonance frequencies detected by the frequency characteristic peak detection unit, the rigid body model stored in the storage unit, and a plurality of ones For the frequency characteristic of the N inertia model (N is an integer of 2 or more) calculated from the frequency characteristic formula consisting of one or more of the vibration model of the degree of freedom and the model of the influence degree of the higher order frequency, the measurement is performed. And a frequency characteristic error calculation unit that calculates an error from the frequency characteristic obtained in this manner.
According to a second aspect of the present invention, in the multi-inertia machine model estimation device according to the first aspect, the calculated and measured values of the frequency characteristics of each of the 2-N inertial models obtained by the frequency characteristic error calculating unit A machine model determination unit that compares a calculated error of the frequency characteristic of the rigid body model with the minimum error of the measured value, and determines that the smaller error of one of the models is an actual model. It is characterized by having.
As a result, frequency characteristics can be measured, and the anti-resonance frequency, resonance frequency, and attenuation can be automatically read from the measured frequency characteristic measurement value without using an expensive measurement device. Even if the worker does not have advanced expertise or experience, a machine model for use in simulation or servo adjustment of the motor control device can be easily estimated as a multi-inertia model.
According to a third aspect of the present invention, in the multi-inertia machine model estimation device of the control device according to the first or second aspect, the detection means calculates the position, speed, or acceleration of the motor, or the position, speed, or acceleration of the machine. It is characterized by detecting the amount of movement.
Therefore, even if a detection means using a rotation detector that detects the position, speed, or acceleration of the electric motor or a detection means using a vibration detector is used in the machine, various detection means and control systems can be used. Measure frequency characteristics and use them easily for simulation and servo adjustment of motor control devices, without the need for expensive measurement devices and workers having advanced specialized knowledge and experience. Can be estimated as a multi-inertia model.
[0014]
According to a fourth aspect of the present invention, in the multi-inertia machine model estimation device for an electric motor control device according to any one of the first to third aspects, the frequency characteristic error calculation unit includes the operation command signal in the frequency characteristic formula. And an error between the calculated value of the frequency characteristic and the measured value is calculated by fitting the measured frequency characteristic obtained from the signal of the detection means with a curve.
Since the frequency characteristic is fitted to the curve, an error between the calculated value of the frequency characteristic and the measured value is calculated. Therefore, the measured frequency characteristic, the rigid model, and a plurality of 1 It can be compared with an equation consisting of a model of degree of freedom, and without using an expensive measuring device, even if the operator does not have advanced specialized knowledge and experience, frequency characteristics can be measured, and simulation and motors can be easily performed. A machine model to be used for servo adjustment of the control device can be estimated as a multi-inertia model.
In addition, it is possible to calculate the error between the calculated value and the measured value of the frequency characteristics, distinguish between rigid and multi-inertia systems by comparison, and determine the model order of the multi-inertia system
In addition, it is possible to provide an inexpensive multi-inertia machine model estimation device for an electric motor control device.
[0015]
According to a fifth aspect of the present invention, in the multi-inertia machine model estimation device for a motor control device according to any one of the first to fourth aspects, the controller inputs a signal of the detection means to a (−) terminal. A subtractor and at least one closed loop which receives a signal of the subtractor and outputs a control command, and includes the control command and the operation amount of each component in accordance with a unit system. The motor is controlled, an adder is provided in at least one closed loop, and a signal of the command generator is input to the adder to obtain a measured frequency characteristic.
Because of this, frequency characteristics can be measured with a closed loop assembled, and when a vertical axis is configured, the moving parts of the machine are prevented from falling or misaligned during measurement, and the moving parts of the machine By shifting, it can prevent the problem that the mechanical characteristics change and the resonance frequency shifts and the measurement accuracy of the frequency characteristics deteriorates, and the operator has advanced expertise and experience without using expensive measuring equipment. Even if not, it is possible to measure the frequency characteristics and easily estimate a machine model for use in simulation or servo adjustment of the motor control device as a multi-inertia model.
[0016]
According to a sixth aspect of the present invention, in the motor control device according to any one of the first to fifth aspects, the controller includes at least one closed loop and a switch that opens and closes the at least one closed loop. It is characterized by providing.
This makes it possible to switch between open loop and closed loop in frequency characteristic measurement, and to select the usage method that suits the situation, and the operator has advanced expertise and experience without using an expensive measurement device. Even if it does not have, it can estimate a frequency characteristic and can easily estimate a machine model to be used for a simulation or servo adjustment of an electric motor control device as a multi-inertia model.
A seventh aspect of the invention is the multi-inertia machine model estimation device for a motor control device according to any one of the first to sixth aspects, comprising at least one of the motor control devices, and at least one of the command devices. A command controller for controlling at least one of the motors, wherein at least one of the detection means detects an amount of operation of at least one of the machines, and the frequency characteristic calculation device has at least one It is characterized by obtaining at least two measured frequency characteristics.
Because of this, frequency characteristics using the signals of the detection means of other axes can be measured, interference between axes can be evaluated, multi-inertia models can be estimated from the measured frequency characteristics, and the machine has a multi-axis configuration Can be modeled. Without using an expensive measuring device, even if the operator does not have advanced specialized knowledge and experience, the frequency characteristics can be measured and easily used for simulation and servo adjustment of the motor control device. An inertial machine model can be estimated.
The invention according to claim 8 is the multi-inertia machine model estimation device of the motor control device according to any one of claims 1 to 7, wherein the controller includes an amplifier that generates a driving force, and a filter unit. The control command signal is inserted in front of the filter means, and frequency characteristics are measured.
Since this is the case, the frequency characteristic can be measured by changing the filter means in a suspicious manner, and modeling can be performed assuming that the characteristic of the machine is changed. Moreover, since the frequency characteristic including the effect of the filter means can be measured, the effect can be evaluated.
[0017]
According to a ninth aspect of the present invention, in the multi-inertia machine model estimation device for the motor control device according to the eighth aspect, the controller further includes an adder.
In this way, the use of the filter means can be switched easily. Moreover, since the frequency characteristic including the effect of the filter means can be measured, the effect can be evaluated.
A tenth aspect of the present invention is the multi-inertia machine model estimation device for a motor control device according to any one of the first to seventh aspects, wherein the controller includes a position restriction management means by the detection means, and is input in advance. Based on the position limit value, the operation amount of the machine is limited during frequency characteristic measurement.
Thus, the frequency characteristics can be measured safely without danger of exceeding a predetermined operation amount during operation.
An eleventh aspect of the present invention is the multi-inertia machine model estimation device for the motor control device according to any one of the first to seventh aspects, wherein the frequency characteristic calculation device includes a display device, an input device, and a storage device. Any one or more of the above is provided.
As a result, the measured frequency characteristics and multi-inertia model results can be observed and confirmed, and the frequency characteristics formulas and position limit values input in advance and various switches can be easily changed. It is possible to store the measured frequency characteristic and the result of the model with multiple inertia.
[0018]
DETAILED DESCRIPTION OF THE INVENTION
(First embodiment)
Hereinafter, specific embodiments of the present invention will be described with reference to FIGS.
FIG. 1 is an overall configuration diagram of a multi-inertia machine model estimation device of an electric motor control device showing an embodiment of the present invention.
In FIG. 1, 1 is a command controller, 2 is a command device, 3 is a controller, 4 is an electric motor, 5 is a detection means, 6 is a machine, 7 is a movable part of the machine, 10 is a frequency characteristic calculation device, and 11 is a frequency. It is a characteristic formula.
The target machine has a two-axis XY configuration, the X-axis is a semi-closed control system that controls the motor 4 with a detection means 5 that detects the operation amount of the motor 4, and the Y-axis detects the operation amount of the motor 4. This is a fully closed control system provided with both the detection means 5 and the detection means 5 for detecting the operation amount of the operating part 7 of the machine. Due to the difference between the two-axis configuration and the control system, the X-axis includes a command device 2a, a controller 3a, a motor 4a, and a detection means 5a, and the Y-axis includes a command device 2b, a controller 3b, a motor 4b, and a detection Means 5b and detection means 5c are provided.
FIG. 2 is a block diagram of the configuration diagram of FIG. The main difference in expression from FIG. 1 is the electric motor 4 and the machine 6. In addition, 31a is an X-axis position controller, 32a is an X-axis speed controller, 31b is a Y-axis position controller, 32b is an X-axis speed controller, and 40 is a switch.
1 and 2 constitute a closed loop of speed and position, but are opened by a switch 40.
The electric motor 4 and the machine 6 are composed of each element Hi, j as a multi-input multi-output in consideration of the number and position of the electric motors 4 and the detecting means 5, and the frequency characteristic calculation device determines each element Hi, j Measure as frequency characteristics.
Each element Hi, j of the machine will be described in detail in the description of the operation.
[0019]
FIG. 3 shows a flowchart relating to the measurement of the frequency characteristic in the calculation operation procedure of the frequency characteristic calculation apparatus.
In FIG. 3, 10A is a frequency characteristic measurement unit, 10B is a frequency characteristic peak detection unit, 10C is an attenuation estimated value analysis unit, 10D is a frequency characteristic error calculation unit, and 10E is a machine model determination unit.
FIG. 4 is a flowchart regarding the operation of determining the machine model based on the measured frequency characteristic value from the measurement of the frequency characteristic according to the embodiment of the present invention.
The operation will be described with reference to the flowchart of FIG.
As for the calculation operation procedure of the frequency characteristic calculation apparatus 1, each step from step ST1 to step ST5 for measuring the frequency characteristic is compared with the model based on the frequency characteristic formula and the measured frequency characteristic is modeled. There are steps ST6 to ST11.
Of the steps, the processes of steps ST4 and ST5 correspond to the frequency characteristic measurement unit 10A of the frequency characteristic calculation device 10 shown in FIG. 3, and the processes of steps ST6 and ST7 correspond to the frequency characteristic peak detection unit 10B. is doing. Further, the process of step ST8 corresponds to the estimated attenuation value analysis unit 10C, the process of steps ST9 and ST10 corresponds to the frequency characteristic error calculation unit 10D, and the process of step ST11 corresponds to the machine model determination unit 10E. .
Steps ST2b and ST2c and steps ST3b and ST3c will be described in the second embodiment.
First, measurement of frequency characteristics in steps ST1 to ST5 will be described.
First, in step ST1, the command device 2 creates an operation command signal. In the case of multiple axes, the command controller 1 controls a plurality of command devices 2.
Next, in step ST <b> 2, the operation command signal output from the command device 2 is transferred to the controller 3. Thereby, a control signal equivalent to the operation command signal is sent to the electric motor 4, and when the electric motor 4 is operated in step ST2a, the movable portion 7 is operated and vibration is generated.
In step ST <b> 3, the detection unit 5 detects the operation signal of the electric motor 4, and transfers the detection unit signal to the frequency characteristic calculation device 10 via the controller 3.
If the operation command signal is completed, the operation of the electric motor is completed in step ST3a. Then, in step ST4, the frequency characteristic calculation device 10 performs, for example, FFT calculation on the operation command signal and the detection means signal, and performs frequency analysis.
In step ST5, the frequency characteristic is calculated from the operation command signal and the detection means signal subjected to frequency analysis in the frequency characteristic calculation device 10. By the processing so far, the measurement of the frequency characteristic is completed.
[0020]
Since the present embodiment has a two-axis machine configuration, a plurality of frequency characteristics can be obtained as machine elements Hi, j.
The machine element Hi, j shown in the block diagram of FIG. 2 will be described.
In the two-axis machine, when the X-axis controller 3a gives the driving force T1 to the electric motor 4a, the machine 6 including the movable part 7 responds. A response is returned not only to the X-axis detection means 5a but also to the Y-axis detection means 5b and the detection means 5c depending on the characteristics of the machine. Similarly, when the Y-axis controller b gives the driving force T2 to the motor 4b, depending on the characteristics of the machine, not only the Y-axis detection means 5b and the detection means 5c but also the X-axis detection means 5a responds. return.
The response Rj when a certain driving force Ti is applied to the element MoHi, j of the machine 6 is as shown in Expression (13).
Rj = Hi, j · Ti Formula (13)
So,
Mechanical element H having characteristics from the X-axis motor 4a to the X-axis detection means 5a_{EX, X},
Mechanical element H having characteristics from the X-axis motor 4a to the Y-axis detection means 5b_{EY, X},
Mechanical element H having characteristics from the X-axis motor 4a to the Y-axis detection means 5c_{LY, X},
Mechanical element H having characteristics from the Y-axis motor 4b to the X-axis detection means 5a_{EX, Y},
Mechanical element H having characteristics from the Y-axis motor 4b to the Y-axis detection means 5b_{EY, Y},
Mechanical element H having characteristics from the Y-axis motor 4b to the Y-axis detection means 5c_{LY, Y}, The response R of the X-axis detection means 5a given the X-axis driving force T1._{Ex, 1}Machine element H_{EX, X}The relationship with
R_{Ex, 1}= H_{EX, X}・ T₁ Equation (13.1) becomes
Response R of the Y-axis detection means 5b given the X-axis driving force T1_{EY, 1}Machine element H_{EY, X}The relationship with
R_{EY, 1}= H_{EY, X}・ T₁ Equation (13.2)
Response R of the Y-axis detecting means 5c given the X-axis driving force T1_{LY, 1}Machine element H_{LY, X}The relationship with
R_{LY, 1}= H_{LY, X}・ T₁ Equation (13.3) becomes
Response R of X-axis detection means 5a given Y-axis driving force T2_{Ex, 2}Machine element H_{EX, Y}The relationship with
R_{Ex, 2}= H_{EX, Y}・ T₂Equation (13.4) becomes
Response R of Y-axis detection means 5b given Y-axis driving force T2_{EY, 2}Machine element H_{EY, Y}The relationship with
R_{EY, 2}= H_{EY, Y}・ T₂Equation (13.5)
Response R of Y-axis detection means 5c given Y-axis driving force T2_{LY, 2}Machine element H_{LY, Y}The relationship with
R_{LY, 2}= H_{LY, Y}・ T₂It can be expressed as equation (13.6).
[0021]
Assuming the case where the X-axis motor 4a and the Y-axis motor 4b are operated simultaneously, if these are collected for each detection means 5,
The signal R1 detected by the X axis detection means 5a is
R1 = R_{Ex, 1}+ R_{Ex, 2} Equation (14.1)
The signal R2 detected by the Y-axis detection means 5b is
R2 = R_{EY, 1}+ R_{EY, 2}  Equation (14.2)
The signal R3 detected by the Y-axis detection means 5c is
R3 = R_{LY, 1}+ R_{LY, 2}  Equation (14.3) is obtained.
The machine 6 includes the mover of the electric motor 4 as an element included in the machine.
That is, the equation (14) is an element H of the machine 6 including the electric motor 4._{i, j}And driving force T_jAnd signal R of detection means 5_i, Element H_{i, j}Can be measured as a frequency characteristic.
[Formula 6]

[0022]
Machine elements H_{i, j}The relationship is as described above. In the embodiment of the present invention, six frequency characteristics can be obtained.
Actual machine elements H_{i, j}In other words, there are roughly two types of procedures for measuring six frequency characteristics.
・ When X-axis motor 4a and Y-axis motor 4b are operated simultaneously
・ When the X-axis motor 4a and the Y-axis motor 4b are operated by a single axis
There is.
(1) When X-axis motor 4a and Y-axis motor 4b are operated simultaneously
In order to obtain each element Hi, j of the machine from the equation (14),
Adjoint (complex conjugate and transpose) matrix [T] of a column vector [T] consisting of driving forces (torques) on the X and Y axes^hIs multiplied from the right side of both sides of the equation (14),
[Expression 7]

[0023]
・ When the X-axis motor 4a and the Y-axis motor 4b are operated by a single axis
In this case, the motor 4 is driven for each single axis, and the other motor 4 is not driven.
When driving the electric motor 4a only from the X axis from the equation (14), the equation (14a) is obtained.
[Equation 8]

Therefore, the frequency characteristics of each element Hi, j of the machine can be measured.
In step ST3, since the controller 3 holds the operation command signal and the detection signal, the detection means 5 detects the operation signal of the electric motor 4, and calculates the frequency characteristic calculation of the detection means signal via the controller 3. Although it is assumed that the signal is transferred to the device 10, a signal may be transferred directly from the command device 2 and the detection means 5 to the frequency characteristic calculation device 10.
[0024]
Next, the modeling of mechanical characteristics in steps ST6 to ST11 will be described. Machine element, that is, measured frequency characteristic H_{EX, X}, H_{EY, X}, H_{LY, X}, H_{EX, Y}, H_{EY, Y}, H_{LY, Y}Each can be applied to step ST6 to step ST11.
First, the mechanical property model can be divided into a rigid body model and a multi-inertia model.
For supplementary explanation, a rigid body model and a multi-inertia model will be described with reference to FIGS.
FIG. 11 is a diagram showing a rigid body model.
That is, the electric motor 4, the machine 6, and the movable portion 7 shown in FIG. 1 are approximated to a simple rigid load.
FIG. 12A is a diagram showing an outline of the two-inertia model.
That is, the electric motor 4, the machine 6, and the movable part 7 shown in FIG. 1 are connected to the two masses including the motor side load (J1) and the load side load (J2), the two masses, and the spring (K : Spring constant) and damping (D: damping constant) and more approximate to a two-inertia model.
FIG. 12B is a diagram showing a three-inertia model. Here, (i) of FIG. 12 (b) shows the electric motor 4, the machine 6, and the movable part 7 shown in FIG. 1 in three masses consisting of three loads (J1), (J2), and (J3). The three masses and the spring connecting the three loads (K: spring constant) and damping (D: damping constant) are approximated to a three-inertia model. Also, (ii) in FIG. 12 (b) shows the electric motor 4, the machine 6, and the movable part 7 with the two-inertia model composed of the electric motor side load (J1) and the load side load (J2) and the reaction force of the load side load. It consists of a load (J3) which is the base of the machine part to be received, and is a three-inertia model approximated by a total of three masses, a total of three springs (K: spring constant) and damping (D: damping constant). Yes.
Further, FIG. 12 (c) shows the motor 4, the machine 6, and the movable part 7 with a total of 17 masses, a total of 18 springs (K: spring constant) and damping (D: damping constant), and a load counter. This is an example of a 17-inertia model showing the relationship of receiving force.
Among these, in the rigid body model shown in FIG. 11, the frequency characteristic H from the operation command signal to the speed signal of the detection means 5 is shown._rThe model formula is given by formula (16).
[Equation 9]

However, as the model order increases, the combination of spring and mass, the distribution of load values (moment of inertia and weight), and frequency characteristics H_rThe model formula becomes complicated.
[0025]
FIG. 5 is a diagram showing a one-degree-of-freedom model in the embodiment of the present invention.
The vibration generated by the multi-inertia machine is considered to be a collection of a plurality of single-degree-of-freedom systems as shown in FIG._rThe frequency characteristic formula is constructed so that The amplitude and phase of the one-degree-of-freedom system may be adjusted for each resonance frequency, and the sum of the one-degree-of-freedom system model may be set in accordance with the measured frequency characteristics. If the mechanical characteristic is proportional viscosity damping, the frequency characteristic equation is expressed by equation (18) in the speed response.
[Expression 10]

here,
H_{i, j}: Frequency characteristics, R^(V) _j: Speed signal of detection means 5, T_i: Driving force, ζ_r: R-th order attenuation ratio, ω_r: R-order natural frequency, A_r: R-th residue,
J: moment of inertia or mass of rigid load,
N: number of natural frequencies (resonance), s: Laplace operator
If the unit of motion changes and the position (displacement) response, the frequency characteristic equation becomes equation (19).
## EQU11 ##

Where R^(A) _j: Acceleration signal of detection means 5
Considering the influence B of the frequency higher than the frequency domain to be modeled
The frequency characteristic formula may be the following formulas (21) to (23).
[Expression 12]

[0026]
As the general structural viscous damping, the frequency characteristic equation may be changed to the following equations (24) to (26) in consideration of the influence degree B of the higher-order frequency, or the term B may be omitted. .
[Formula 13]

The degree of influence (residual rigidity) B of this higher order frequency can be stored in the memory 11 as one of the frequency characteristic equations of FIG. Therefore, the frequency characteristic formula of the memory 11 can be (i) a rigid body model, (ii) a plurality of one-degree-of-freedom vibration models, and (iii) a model of the influence of higher-order frequencies. It consists of at least one of these.
If the influence of the moving part 7 of the machine is large, the influence 1 / (J · s) of the rigid system is always applied. However, the influence of the moving part 7 of the machine depends on the position of the detection means in relation to the driving force. When there is no, the influence 1 / (J · s) of the rigid system may be omitted.
[0027]
In step ST6, the number of multi-inertia is related to the number of resonances, so the peak on the peak side and the peak on the valley side are calculated from the measured frequency characteristics.
As a peak calculation method, a known method such as a complex spectrum interpolation method or a smoothing differentiation method may be used.
In step ST7, it is determined whether there is a peak detected in step ST6.
If the peak on the mountain side and the peak on the valley side cannot be detected, it can be determined as a rigid body.
In order to approximate the rigid body model, it is only necessary to know the sum J of the rigid body load or the load on each axis including the motor side load 4 and the machine movable part.
Since the rigid body model is the equation (16), the rigid body load J can be obtained from the frequency characteristics, that is, the shape of the machine element H. When the frequency characteristic of the rigid model is graphed, the gain characteristic has a downward slope to the right.
If it is determined in step ST7 that there is a peak, the processes in steps ST8 and ST10 are performed.
In step ST8, attenuation can be estimated based on a known attenuation estimation method by using the detected peak frequency. If the influence of the movable part 7 of the machine is large even when there is a peak, the characteristic of the rigid system appears in the low frequency region of the measured frequency characteristic. Therefore, if the load component J of the rigid system is uncertain, In the range limited to the low frequency region lower than the peak on the valley side, the load component J of the rigid system may be calculated by applying the equation (16) to the measured frequency characteristics and using the least square method.
In step ST9, the temporarily set peak is set to the number of peaks or the number of selected peaks according to any one of the frequency characteristic formulas input in advance to the frequency characteristic calculation apparatus 10, that is, formulas (18) to (26). The curve is fitted only by the number, and an error between any of the equations (18) to (26) and the measured value is calculated.
[0028]
Since the load component J and the peak on the peak side and the peak on the valley side, that is, the resonance and anti-resonance frequencies and the attenuation are known, for example, all the denominator terms in the equation (18) are known. What is necessary is just to determine the phase which is and the amplitude which is magnitude | size so that it may match a measured value.
A curve can be fitted by determining each value for each of the resonance frequencies and any one of the frequency characteristic formulas (18) to (26) that are one of the frequency characteristic formulas input to the frequency characteristic computing device 10 and adding them together.
Step ST10 is a step of processing in parallel with steps ST8 and ST9, and even when a peak is detected, a rigid body model is obtained by equation (16), which is one of the frequency characteristic equations input to the frequency characteristic arithmetic unit 10. An error between the curve fitting result and the measured frequency characteristic is calculated in advance.
Next, in step ST11, the error of the multi-inertia model is compared with the error of the rigid body model, and when the error of the rigid body model is small, it can be determined as the rigid body model.
When the error of the multi-inertia model is small, it can be determined as a multi-inertia model. That is, the error between any one of the equations (18) to (26) and the measured frequency characteristic is compared with the error between the equation (16) and the measured frequency characteristic, and the rigid system equation (16) is compared with the multi-inertia. By modeling into any one of the equations (18) to (26), it is possible to determine which error is smaller and which is optimal for the model.
[0029]
FIG. 6 is a graph showing an example of the measured frequency characteristic in the embodiment of the present invention.
FIG. 7 is a graph showing an example of a frequency characteristic obtained by fitting a curve into a multi-inertia model in the embodiment of the present invention.
FIG. 8 is a block diagram showing an example of frequency characteristics in the multi-inertia model according to the embodiment of the present invention.
FIGS. 9 and 10 illustrate a rigid body model and 16 natural frequencies (resonances) from the peak, taking into account a plurality of measured frequency characteristics from the measured frequency characteristics, and estimating the attenuation thereof. This is an example of curve fitting according to (18). In this way, a high-order multi-inertia model can be estimated.
If the frequency range of the measured frequency characteristic is expanded, the multi-inertia model can be further increased by the higher-order resonance frequency and attenuation and the above equation (18).
When the determination of the model is completed, the result can be output to the output device 21 connected to the frequency characteristic calculation device 10, and can be used for simulation and adjustment of the motor control device. In addition, the result can be stored in the storage device 23.
The input device 22 can change a frequency characteristic formula input in advance and perform other operations.
Measured frequency characteristics H_{EX, X}, H_{EY, X}, H_{LY, X}, H_{EX, Y}, H_{EY, Y}, H_{LY, Y}2 can be modeled, the numerical values of the machine elements in the block diagram of FIG. 2 can be digitized, which can be used for simulation of a multi-axis configuration and adjustment of an electric motor control device.
[0030]
(Second Embodiment)
Next, a second embodiment of the present invention will be described with reference to FIG.
FIG. 9 is a block diagram showing the configuration of the second embodiment.
In FIG. 9, 1 is a command controller, 2 is a command device, 3 is a controller, 4 is an electric motor, 5 is a detection means, 6 is a machine, 7 is a movable part of the machine, 10 is a frequency characteristic calculation device, and 11 is a frequency. The characteristic equation, 34 is an adder, 35 is an amplifier, 36 is a filter means, 38 is a position management means, 39 is a position restriction management means, and 40 is a switch.
In this way, a single-axis open loop configuration is used, and the frequency characteristics can be measured according to steps ST1 to ST5 in the flowchart of FIG. 4 as in the first embodiment. The difference from the first embodiment is that the position management means 38 and the position restriction management means 39 are provided. Therefore, in measuring frequency characteristics, the motor 4 operates in step ST2a, and the operation of the motor in step ST3a is performed. During the completion, it is determined in step ST2b that the motion detection amount exceeds the position limit value L input in advance, and the position limit management means 39 is operated in step ST2c. The driving force can be cut off and the electric motor can be stopped. When the operation amount is large, it is possible to avoid the danger that the movable part 7 collides with another part of the machine.
If the switch 40 is switched so that the driving force T is applied to the electric motor 4 or the machine 6 through the filter means 36, the frequency characteristics including the characteristics of the filter means 36 can be measured.
From step ST6 to step ST11, the multi-inertia model can be estimated as in the first embodiment, and when the model determination is completed, the result can be output to the output device 21 connected to the frequency characteristic calculation device 10, It can be used for simulation and adjustment of motor control devices. In addition, the result can be stored in the storage device 23.
The input device 22 can change a frequency characteristic formula and a position limit value L that are input in advance and perform other operations.
[0031]
(Third embodiment)
Furthermore, a third embodiment of the present invention will be described with reference to FIG.
FIG. 10 is a block diagram showing the configuration of the third embodiment.
In FIG. 10, 1 is a command controller, 2 is a command device, 3 is a controller, 4 is an electric motor, 5 is a detection means, 6 is a machine, 7 is a movable part of the machine, 10 is a frequency characteristic calculation device, and 11 is a frequency. The characteristic equation, 31 is a position controller, 32 is a speed controller, 33 is a subtractor, 34 is an adder, 35 is an amplifier, 36 is a filter means, 37 is a differentiator, and 40 is a switch.
FIG. 10 has a single-axis closed loop configuration. The difference from FIG. 9 which is the second embodiment is that there are two closed loops including a position controller 31, a speed controller 32 and a differentiator 37. The command C = 0, and the operation command signal from the command device 2 is input to the adder 34 in the two closed loops.
Thus, the sum of the signal of the detecting means 5, the driving force signal output from the amplifier 35 including the component that circulates in the closed loop, and the operation command signal from the command unit 2 is given to the frequency characteristic calculation device 10. ing.
Further, since the position command C = 0 is given by forming a position loop, the position management means 38 and the position restriction management means 39 are not provided.
The operation of the third embodiment is the same as that of the first and second embodiments, and operates according to the flowchart of FIG. The frequency characteristics can be measured according to steps ST1 to ST5 in the flowchart of FIG. If the switch 40 is switched so that the driving force T is applied to the electric motor 4 or the machine 6 through the filter means 36, the frequency characteristics including the characteristics of the filter means 36 are measured as in the second embodiment. Can do. Further, similarly to the first and second embodiments, the multi-inertia model can be estimated from the frequency characteristics according to steps ST6 to ST11, and when the model determination is completed, the output device 21 connected to the frequency characteristic calculation device 10 is completed. The result can be output to the system and used for simulation and adjustment of the motor control device.
[0032]
【The invention's effect】
As described above, according to the invention of the multi-inertia machine model estimation device for a motor control device according to claim 1, upon receipt of a command device for creating a control command signal and a control command signal created by the command device. A motor control device having a controller for driving an electric motor and a detection means for detecting an operation amount of a machine driven by the electric motor. An operation amount detected by the detection means and a control command signal created by the command device In a multi-inertia machine model estimation device for an electric motor control device comprising a frequency characteristic calculation device for calculating and measuring a frequency characteristic, the frequency characteristic calculation device includes a rigid body model inputted in advance and a plurality of one-degree-of-freedom models. A storage unit for storing a frequency characteristic formula including at least one of a vibration model and an influence model of a higher-order frequency, and a protrusion including a resonance frequency and an anti-resonance frequency of the frequency characteristic Stored in the storage unit, a frequency characteristic peak detection unit that automatically calculates the state, an attenuation estimation value analysis unit that estimates attenuation from a plurality of resonance frequencies or anti-resonance frequencies detected by the frequency characteristic peak detection unit, and Frequency characteristics of an N inertia model (N is an integer of 2 or more) calculated from a frequency characteristic formula including any one or more of the rigid body model, a plurality of vibration models with one degree of freedom, and a model of the influence degree of higher-order frequencies. And a frequency characteristic error calculation unit for calculating an error from the frequency characteristic obtained by the measurement. According to the invention of claim 2, the multi-inertia machine of claim 1 is provided. In the model estimation apparatus, the minimum error between the calculated value and the measured value of the frequency characteristic of each of the inertia models 2 to N obtained by the frequency characteristic error calculation unit, the calculated value of the frequency characteristic of the rigid body model, Compared to the minimum measurement error, the one with the smaller error of either model is judged as the actual model, so the frequency characteristics can be measured, and from the measured frequency characteristic measurement values By automatically reading the anti-resonance frequency, resonance frequency, and attenuation, it is possible to easily perform simulations and servo adjustments of motor control devices without using high-level specialized knowledge and experience without using expensive measurement devices. It becomes possible to estimate a machine model for use as a multi-inertia model.
[0033]
According to a third aspect of the present invention, in the multi-inertia machine model estimation device of the control device according to the first or second aspect, the detection means is a position, speed or acceleration of the electric motor, or a position, speed or speed of the machine. Since the acceleration is detected and used as the operation amount, detection means using a rotation detector that detects the position, speed, or acceleration of the electric motor, or using a detection means using a vibration detector in the machine, or various detection means The frequency characteristics can be easily measured and servos of the motor control device can be easily measured without using an expensive measuring device and without the operator having advanced specialized knowledge and experience. The machine model to be used for adjustment can be estimated as a multi-inertia model.
According to a fourth aspect of the present invention, in the multi-inertia machine model estimation device for an electric motor control device according to any one of the first to third aspects, the frequency characteristic error calculating unit includes the operation of the frequency characteristic equation in the frequency characteristic equation. Since the measured frequency characteristic obtained from the command signal and the signal of the detection means is fitted to the curve to calculate the error between the calculated value of the frequency characteristic and the measured value, the measured frequency characteristic, the rigid model, and a plurality of 1 It can be compared with an equation consisting of a model of degree of freedom, and without using an expensive measuring device, even if the operator does not have advanced specialized knowledge and experience, frequency characteristics can be measured, and simulation and motors can be easily performed. A machine model to be used for servo adjustment of the control device can be estimated as a multi-inertia model.
Also, it is possible to calculate the error between the calculated value of frequency characteristics and the measured value, and to distinguish between rigid and multi-inertia systems by comparison and to determine the model order of multi-inertia systems
In addition, it is possible to provide an inexpensive multi-inertia machine model estimation device for an electric motor control device.
[0034]
According to a fifth aspect of the present invention, in the multi-inertia machine model estimation device for the motor control device according to any one of the first to fourth aspects, the controller sends a signal of the detection means to a (−) terminal. An input subtractor and at least one closed loop that receives a signal from the subtractor and outputs a control command are provided, so that the control command and the operation amount of each component coincide with each other in accordance with a unit system. The adder is provided in at least one closed loop, and the measured frequency characteristics are obtained by inputting the command generator signal to the adder. The frequency characteristics can be measured as it is, and when a vertical axis is configured, the moving parts of the machine are prevented from falling or misaligned during the measurement, and the machine moving parts are displaced to resonate. frequency The frequency characteristic measurement accuracy can be prevented from being lost and the frequency characteristic can be easily measured without using an expensive measuring device and without having high level of expertise and experience. In addition, a machine model to be used for simulation and servo adjustment of an electric motor control device can be estimated as a multi-inertia model.
According to a sixth aspect of the present invention, in the motor control device according to any one of the first to fifth aspects, the controller opens and closes at least one closed loop and at least one closed loop. Since it has a switch, it can switch between open loop and closed loop in frequency characteristic measurement, can select the usage method according to the situation, without using an expensive measurement device, the operator does not have advanced expertise and experience However, by measuring the frequency characteristics, a machine model for use in simulation and servo adjustment of the motor control device can be easily estimated as a multi-inertia model.
According to a seventh aspect of the present invention, in the multi-inertia machine model estimation device for a motor control device according to any one of the first to sixth aspects, at least one of the motor control devices is provided, and at least one of the motor control devices is provided. A command controller that controls the command device, operates at least one of the electric motors, at least one or more of the detection means detect an operation amount of at least one or more machines, the frequency characteristic calculation device, Since at least one measured frequency characteristic is obtained, it is possible to measure frequency characteristics using signals from other axis detection means, evaluate interference between axes, and estimate a multi-inertia model from the measured frequency characteristics And the machine can be modeled as a multi-axis configuration. In addition, without using an expensive measuring device, it is possible to measure frequency characteristics and easily use it for simulations and servo adjustments of motor control devices without the need for highly specialized knowledge and experience. In addition, a multi-inertia machine model can be estimated.
[0035]
According to an eighth aspect of the present invention, in the multi-inertia machine model estimation device for an electric motor control device according to any one of the first to seventh aspects, the controller includes an amplifier for generating a driving force and a filter means. Since the control command signal is inserted from the front of the filter means and the frequency characteristics are measured, the frequency characteristics can be measured by changing the filter means in a suspicious manner, and the machine characteristics are changed as a model. it can. Moreover, since the frequency characteristic including the effect of the filter means can be measured, the effect can be evaluated.
According to the ninth aspect of the present invention, in the multi-inertia machine model estimation device for the motor control device according to the eighth aspect, the controller further includes an adder, so that the use of the filter means can be switched easily. In addition, since the frequency characteristics including the effect of the filter means can be measured, the effect can be evaluated.
According to a tenth aspect of the present invention, in the multi-inertia machine model estimation device for a motor control device according to any one of the first to seventh aspects, the controller includes a position restriction management means by the detection means, Since the operation amount of the machine is limited during the frequency characteristic measurement based on the position limit value input in advance, the frequency characteristic can be measured safely without risk of exceeding the predetermined operation amount during the operation. It becomes like this.
According to the eleventh aspect of the present invention, in the multi-inertia machine model estimation device for the motor control device according to any one of the first to seventh aspects, the frequency characteristic calculation device includes a display device, an input device, and Since any one or more of the storage devices are provided, the measured frequency characteristics and the results of the multi-inertia model can be observed and confirmed. Various switches can be easily changed, and the measured frequency characteristics and multi-inertial model results can be saved.
[Brief description of the drawings]
FIG. 1 is an overall configuration diagram of an electric motor control device including a multi-inertia machine model estimation device according to an embodiment of the present invention.
FIG. 2 is a block diagram showing the configuration of FIG.
FIG. 3 shows a flow chart relating to frequency characteristic measurement in the calculation operation procedure of the frequency characteristic calculation apparatus.
FIG. 4 is a flowchart related to an operation of determining a multi-inertia machine model based on a measured frequency characteristic value in a calculation operation procedure of the frequency characteristic calculation apparatus according to the embodiment of the present invention.
FIG. 5 is a diagram showing a one-degree-of-freedom model in the embodiment of the present invention.
FIG. 6 is a graph showing an example of measured frequency characteristics in the embodiment of the present invention.
FIG. 7 is a graph showing an example of a frequency characteristic obtained by fitting a curve into a multi-inertia model in the embodiment of the present invention.
FIG. 8 is a block diagram of an example of a frequency characteristic that is a multi-inertia model according to the embodiment of the present invention.
FIG. 9 is a block diagram illustrating a configuration of an electric motor control device including a multi-inertia machine model estimation device according to a second embodiment of the present invention.
FIG. 10 is a block diagram illustrating a configuration of an electric motor control device including a multi-inertia machine model estimation device according to a third embodiment of the present invention.
FIG. 11 is a diagram showing a rigid body model.
FIG. 12 is a diagram showing a multi-inertia model.
FIG. 13 is an explanatory diagram for explaining an example of a resonance mechanism of a conventional technique.
FIG. 14 is a block diagram for explaining the principle and example of the prior art.
FIG. 15 is a block diagram of a second embodiment of the prior art.
[Explanation of symbols]
1: Command controller
2: Commander
2a: X-axis commander
2b: Y-axis command device
3: Controller
3a: X-axis controller
3b: Y-axis controller
4: Electric motor
4a: X-axis motor
4b: Y-axis motor
5: Detection means
5a: X-axis motor detection means
5b: Y axis motor detection means,
5c: Y-axis mechanical movement amount detecting means
6: Machine
7: Machine moving parts
10: Frequency characteristic calculation device
10A: Frequency characteristic measurement unit
10B: Frequency characteristic peak detector
10C: estimated attenuation value analysis unit
10D: Frequency characteristic error calculation unit
10E: Machine model determination unit
11: Storage unit for storing frequency characteristic formula
21: Output means
22: Operating means
23: Storage device

Claims (11)

An electric motor comprising: a command device that creates a control command signal; a controller that drives a motor in response to the control command signal created by the command device; and a detection unit that detects an operation amount of a machine driven by the motor. To the control unit,
In the multi-inertia machine model estimation device of the motor control device comprising a frequency characteristic calculation device that calculates and measures the operation amount detected by the detection means and the control command signal created by the commander to obtain a frequency characteristic,
The frequency characteristic calculation device includes a storage unit that stores a frequency characteristic equation including any one or more of a rigid body model, a plurality of one-degree-of-freedom vibration models, and a higher-order frequency influence model that are input in advance.
A frequency characteristic peak detector that automatically calculates a protrusion shape comprising a resonance frequency and an anti-resonance frequency of the frequency characteristic;
An attenuation estimation value analysis unit that estimates attenuation from a plurality of resonance frequencies or anti-resonance frequencies detected by the frequency characteristic peak detection unit;
An N inertia model (N is 2) calculated from a frequency characteristic equation including any one or more of the rigid body model, a plurality of vibration models of one degree of freedom, and a model of the influence degree of higher-order frequencies stored in the storage unit. A multi-inertia machine model of an electric motor control device, comprising: a frequency characteristic error calculating unit that calculates an error from the frequency characteristic obtained by the measurement with respect to the frequency characteristic of the above integer) Estimating device. The minimum error between the calculated value and the measured value of the frequency characteristic of each inertia model of 2 to N obtained in the frequency characteristic error calculation unit is compared with the calculated value of the frequency characteristic of the rigid body model and the minimum error of the measured value. 2. The multi-inertia machine model estimation apparatus according to claim 1, further comprising a machine model determination unit that determines that one of the models having a smaller error is an actual model. 3. The multi-inertia machine of the control device according to claim 1, wherein the detecting means detects the position, speed or acceleration of the electric motor, or the position, speed or acceleration of the machine and makes the operation amount. Model estimation device. The frequency characteristic error calculating unit calculates an error between a calculated value of the frequency characteristic and an error of the measured value by fitting the measured frequency characteristic obtained from the operation command signal and the signal of the detection unit to the frequency characteristic formula. The multi-inertia machine model estimation device for an electric motor control device according to any one of claims 1 to 3, wherein the multi-inertia machine model estimation device is provided. The controller includes a subtractor that inputs a signal of the detection means to a (−) terminal and at least one closed loop that receives the signal of the subtractor and outputs a control command. And controlling the electric motor so that the operation amount of each component coincides with the control command, an adder is provided in at least one closed loop, and a signal of the command generator is input to the adder. 5. The multi-inertia machine model estimation device for an electric motor control device according to claim 1, wherein the measured frequency characteristic is obtained. The motor controller according to claim 1, wherein the controller includes at least one closed loop and a switch that opens and closes the at least one closed loop. A command controller that controls at least one of the motor control devices, operates at least one of the motors, and has at least one or more detection means. The multi-inertia of the motor control device according to any one of claims 1 to 6, wherein the operation amount of the machine is detected, and the frequency characteristic calculation device obtains at least one measured frequency characteristic. Machine model estimation device. 8. The controller according to claim 1, wherein the controller includes an amplifier that generates a driving force and a filter unit, and the control command signal is inserted from the front of the filter unit to measure frequency characteristics. The multi-inertia machine model estimation device for the motor control device according to claim 1. 9. The multi-inertia machine model estimation device for an electric motor control device according to claim 8, wherein the controller includes an adder. 2. The controller according to claim 1, wherein the controller includes a position limit management unit based on the detection unit, and operates by limiting an operation amount of the machine during measurement of frequency characteristics based on a position limit value input in advance. The multi-inertia machine model estimation apparatus of the electric motor control apparatus of any one of 1-7. The motor control device according to any one of claims 1 to 7, wherein the frequency characteristic calculation device includes any one or more of a display device, an input device, and a storage device. Multi-inertia machine model estimation device.

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