CN113472262A - MTPA control method for identifying d-q axis inductance parameters of permanent magnet synchronous motor by adopting fuzzy logic control - Google Patents

MTPA control method for identifying d-q axis inductance parameters of permanent magnet synchronous motor by adopting fuzzy logic control Download PDF

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CN113472262A
CN113472262A CN202110629409.XA CN202110629409A CN113472262A CN 113472262 A CN113472262 A CN 113472262A CN 202110629409 A CN202110629409 A CN 202110629409A CN 113472262 A CN113472262 A CN 113472262A
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model
control
axis inductance
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刘国海
安兴科
陈前
赵文祥
宋向金
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Jiangsu University
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/24—Vector control not involving the use of rotor position or rotor speed sensors
    • H02P21/28—Stator flux based control
    • H02P21/30—Direct torque control [DTC] or field acceleration method [FAM]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/0003—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control
    • H02P21/001—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control using fuzzy control
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/14—Estimation or adaptation of machine parameters, e.g. flux, current or voltage
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/14—Estimation or adaptation of machine parameters, e.g. flux, current or voltage
    • H02P21/16—Estimation of constants, e.g. the rotor time constant
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/22—Current control, e.g. using a current control loop
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P25/00—Arrangements or methods for the control of AC motors characterised by the kind of AC motor or by structural details
    • H02P25/02—Arrangements or methods for the control of AC motors characterised by the kind of AC motor or by structural details characterised by the kind of motor
    • H02P25/022—Synchronous motors
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P2207/00—Indexing scheme relating to controlling arrangements characterised by the type of motor
    • H02P2207/05—Synchronous machines, e.g. with permanent magnets or DC excitation

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  • Power Engineering (AREA)
  • Automation & Control Theory (AREA)
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  • Control Of Ac Motors In General (AREA)

Abstract

The invention discloses an MTPA control method for identifying d-q axis inductance parameters of a permanent magnet synchronous motor by adopting fuzzy logic control. The method fully utilizes the advantages of strong robustness of Fuzzy (Fuzzy) logic control, independence of model control and the like, and replaces the traditional PI in the model reference self-adaptation law by Fuzzy-PI, so that the accuracy and the robustness of the traditional model reference self-adaptation identification are improved. Then, the identified d-q axis inductance is updated into an MTPA formula in real time, and the current amplitude under the same torque output is minimized, so that the copper consumption of the motor is reduced, and the operation efficiency is improved; and the parameter is used in model predictive control to replace the original fixed parameter so as to eliminate the disturbance of the motor parameter mismatch on the control system, thereby enhancing the robustness of the control system and obtaining better control performance.

Description

MTPA control method for identifying d-q axis inductance parameters of permanent magnet synchronous motor by adopting fuzzy logic control
Technical Field
The invention relates to the technical field of model prediction control of a permanent magnet synchronous motor, in particular to a control method which adopts fuzzy logic control to improve model reference adaptive parameter identification and is applied to a permanent magnet synchronous motor model prediction MTPA (Maximum torque to current ratio). The method can improve the operation efficiency of the motor and improve the robustness of model predictive control. The permanent magnet motor is suitable for the fields of high-efficiency permanent magnet electric drive device systems, new energy electric vehicles, ship propulsion systems and the like.
Background
The permanent magnet motor has the characteristics of high torque density, high efficiency, high reliability and the like, and is more and more widely applied to the fields of new energy electric automobile traction, aerospace and industrial production. Meanwhile, the parameters of the permanent magnet motor can change due to factors such as the rising of the operating temperature of the motor, magnetic saturation and the like, and the influence of step disturbance of the rotating speed and the torque on the operation of the motor. However, the permanent magnet synchronous motor adopts model predictive control and formula method to realize MTPA control, which both depend on motor parameters seriously. Therefore, updating the actual motor parameters in real time is of great significance for implementing the control method.
For a permanent magnet synchronous machine, some machine parameter nameplates or manuals are provided by manufacturers after the machine is manufactured, but parameters such as d-q axis inductance, flux linkage and the like all need to be measured by users. Even if the same motor body design drawing is provided for different processing manufacturers, the manufacturing process and the level of different processing manufacturers can cause different motor parameters. Although the motor parameters can be measured off-line, the actual parameters of the on-line motor during operation deviate from the off-line identification result. Therefore, the online parameter identification for researching the permanent magnet motor has practical value.
In recent years, the traditional online identification algorithms include a signal injection method, a least square method, a kalman filter algorithm, model reference self-adaptation and the like, and advanced algorithms such as artificial intelligence, a neural network, an ant colony algorithm and the like are all applied to parameter identification, but the algorithms have respective advantages and disadvantages. Because fuzzy logic control is an advanced control method, the fuzzy logic control has the characteristics of good robustness for a time-varying system, independence on an accurate model and the like. By combining the traditional model predictive control and the advanced control algorithm, a better identification result can be realized. And the recognized parameters are updated in real time in the model predictive control depending on the parameters, so that a more ideal control effect can be realized.
Disclosure of Invention
The invention utilizes the advantages and characteristics of strong robustness of Fuzzy-local control (Fuzzy-local control), independence on model control and the like to substitute Fuzzy-PI for the traditional PI self-adaptation law in model reference self-adaptation. Meanwhile, the recognized parameters are updated in real time in the MTPA control realized by model prediction control and formula method. Therefore, the invention provides an MTPA control method for identifying d-q axis inductance parameters of a permanent magnet synchronous motor by adopting fuzzy logic control.
In order to achieve the technical purpose, the invention adopts the following technical scheme:
a MTPA control method for identifying d-q axis inductance parameters of a permanent magnet synchronous motor by adopting fuzzy logic control comprises the following steps:
step 1, firstly, deducing an MTPA current angle calculation formula of a formula method, realizing MTPA control of a permanent magnet motor when motor parameters are updated in real time, and deducing a discretized model predictive control equation of the permanent magnet synchronous motor to realize predictive current control of the motor;
step 2, according to a Popov ultra-stability theory principle, applying the Popov ultra-stability theory principle to a model reference adaptive parameter identification method, then deducing a d-q axis inductance identification adaptive rate expression of the permanent magnet synchronous motor, and when the identified d-q axis inductance is continuously fed back to an adjustable model until the error between the identified d-q axis inductance and a reference model is almost zero, obtaining an identified d-q axis inductance parameter;
step 3, applying fuzzy logic control in model reference self-adaptation, deducing proportional and integral gain adjustment factors of a self-adaptation law, and adjusting d-q axis inductance together with proportional and integral gains in the model reference self-adaptation, so that d-q axis inductance parameters identified by the improved model reference self-adaptation can be obtained;
and 4, using the d-q axis inductance parameters of the improved model reference adaptive identification into an MTPA current angle calculation formula and prediction current control to replace original fixed parameters and update in real time, so that MTPA control of the permanent magnet motor can be realized and the influence caused by inductance mismatch in current prediction control can be eliminated, thereby improving the operating efficiency of the permanent magnet synchronous motor and increasing the parameter robustness of a control system.
Firstly, according to the step 1, the specific process is as follows:
step 1-1, a given rotating speed n of a five-phase permanent magnet motor*Comparing with feedback speed n to obtain speed error e of motorrThe rotating speed error can be obtained by a PI controller to obtain a speed ring error signal I of the five-phase permanent magnet motors；
Step 1-2, rotating reference current i under d-q axis of coordinate systemd *、iq *From the following MTPA current angle equation γMTPACalculating to obtain;
Figure BDA0003101895070000021
Figure BDA0003101895070000022
wherein: l ^ ad(k+1)、L^d(k +1) identifying d-q axis inductance parameters on line respectively, which are given by the Proposed Model Reference Adaptive (PMRAS) parameter on-line identification; psifIs a permanent magnetic flux linkage parameter; i issIs the output of the speed loop PI controller.
Step 1-3, predicting and outputting current i by using cost functionp(k +2) making a difference with the reference current, and selecting a corresponding vector magnitude action PWM to generate a trigger signal for driving the inverter according to the current minimum difference value after the difference is made;
step 1-4, generating S from PWMabcdeBridge arm switching sequence to drive the inverter to generate phase current I by combining the switching trigger sequence and pulse widthabcdeAnd rotor positionSet angle thetae；
Step 1-5, the phase current is changed by Park, and the current I under a natural coordinate system is convertedabcdeConversion into current i in a rotating coordinate systemdq；
Steps 1-6, idqInput signal i controlled by Euler discrete conversion into prediction modelp(k+1)；
1-7, respectively using the input signals of online identification of PMRAS parameters as d-q axis voltage and current u of a rotating coordinate systemdq，idqAnd rotor angular velocity ωmIdentified L ^ sd(k +1) and L ^ Lq(k +1) updating L in model predictive control and MTPAdAnd Lq；
Step 1-8, predicting the current through the identified d-q axis inductance L ^d(k +1) and L ^ Lq(k +1), selecting vector Vi(i ═ 1.. 12), discrete input current ip(k +1) to generate a model predictive control current ip(k+2)；
Figure BDA0003101895070000031
Wherein id、iqIs d-q axis current, ud、uqIs the d-q axis voltage, ix、iyIs a cubic space x-y axis current, ux、uyIs the cubic space x-y axis voltage, RsIs the stator resistance, LlsLeakage inductance, ω rotor angular velocity in the machine,. psifIs a permanent magnetic flux linkage, T is a sampling period, and k is a sampling sequence; l ^ ad(k+1)、L^q(k +1) identifying d-q axis inductance parameters on line respectively, which are given by the Proposed Model Reference Adaptive (PMRAS) parameter on-line identification;
then according to the step 2, the specific processes are respectively as follows:
step 2-1, voltage signal VabcdeThe current signal I can be obtained by a reference modelabcdeWherein i isd、iqIs d-q axis current, ud、uqIs d-q axis voltage, RsIs a stator resistor，Ld、LqIs d-q axis inductance, omegamIs the rotor angular velocity, psi, in the machinefIs a rotor flux linkage, and the current is converted into a current i under a rotating coordinate system under a five-phase natural coordinate system through Park conversiondq；
The model reference adaptive reference model equation is expressed as:
Figure BDA0003101895070000032
step 2-2, voltage signal VabcdeConverting the voltage under a five-phase natural coordinate system into the voltage u under a rotating coordinate system through Park conversiondqAnd used as an input signal of the adjustable model;
the model reference adaptive adjustable model equation is expressed as:
Figure BDA0003101895070000041
wherein, the symbol ^ represents the parameter or signal to be identified;
step 2-3, passing the current signal i of the reference modeldAnd iqAnd the adjustable model current signal idA and iqSubtracting to obtain the output signal of the current error change e (t), and the output signal contains the parameter information to be identified;
Figure BDA0003101895070000042
further, the model reference adaptation rate is derived as follows:
step 3-1, in order to describe the equations briefly, the reference model formula of step 2-1 and the adjustable model formula of step 2-2 are rewritten with the following standard form:
Figure BDA0003101895070000047
wherein the content of the first and second substances,
Figure BDA0003101895070000043
the symbol ". quadrature" represents a differential operator, an adjustable model is established according to a standard form, all parameters are completely consistent with a reference model except d-q axis inductance and current, and an identification value is represented by a symbol "^ quadrature"; in the formula udqIs the stator shaft voltage, idqIs stator shaft current, RsIs the stator phase resistance, omegaeIs the electrical angular velocity, #fIs a motor permanent magnet flux linkage;
step 3-2, the adjustable model is rewritten as follows:
Figure BDA0003101895070000044
wherein the content of the first and second substances,
Figure BDA0003101895070000045
step 3-3, according to the principle of Popov ultra-stability theory, if the feedback system needs to be kept stable, the nonlinear loop should satisfy the following formula
Figure BDA0003101895070000046
And is provided with
Figure BDA0003101895070000051
Where η (0, t) is an integral function, r2Is a finite normal quantity independent of the upper integral limit t, W is a nonlinear feedback input, W1 is an intermediate variable and W1 is-W.
The design purpose of the parameter self-adaptive law is to estimate the needed parameters on line, and then the generalized error of the control system gradually tends to zero through feedback regulation; the design of a general adaptive law usually adopts a proportional-integral regulation principle; according to the hyperstatic law, in order to meet the requirement of hyperstability of a model reference adaptive control system, the requirement of strict and positive implementation of a linear steady forward channel is met, and a nonlinear feedback loop meets an integral inequality, so that a series of derivation processes are omitted, and an adaptive law expression of motor parameters can be obtained.
Firstly, the self-adaptive law of direct axis inductance identification is analyzed, a corresponding calculation formula is given, the derivation process of the quadrature axis self-adaptive law is similar to that of the direct axis self-adaptive law, and the derivation process is omitted.
Figure BDA0003101895070000052
Wherein, L ^ sdFor the direct-axis inductance parameter to be identified, LdFor reference to the direct-axis inductance parameter, R1(τ) is an integral function with respect to τ, R2(τ) is a function of τ.
Then, the above formula is substituted into the equation of step 3-3 to obtain
Figure BDA0003101895070000053
Wherein the symbol ". quadrature" represents a difference operator, ε is a current output difference between a reference model and an adjustable model, ε1The direct axis current output difference of the reference model and the adjustable model is shown.
The above formula is solved by the following theorem,
Figure BDA0003101895070000054
and a positive real constant k is greater than 0, f (t) is a multiplicative function with respect to t,
is given the following formula
Figure BDA0003101895070000055
Then R can be obtained1(τ)＝Ki1ε1udAs long as Kp1Greater than 0, then R2(τ)＝Kp1ε1udThen the adaptation is as follows:
Figure BDA0003101895070000061
wherein, Kp1、Ki1Respectively, the direct axis inductance parameter L ^dProportional integral gain of adaptive rate, Kp2、Ki2Respectively is a quadrature axis inductance parameter L ^qProportional integral gain of adaptation rate.
Further, fuzzy logic control is applied in model reference adaptation, and the specific process is derived as follows:
step 4-1, passing the current signal i of the reference modeldAnd iqAnd the adjustable model current signal idA and iqSubtracting to obtain the output signal of the current error change e (t), and the output signal contains the parameter information to be identified;
Figure BDA0003101895070000062
step 4-2, multiplying the proportional gain Kc and the differential gain Ke by the current error respectively to obtain an input current error E of the fuzzy logic control and a change rate Ec thereof, and then inputting the input current error E and the change rate Ec into the fuzzy logic control;
step 4-3, resolving the fuzzy, converting the implicit fuzzy control set into explicit output through a gravity-center ambiguity resolving technology, wherein the fuzzy reasoning adopts a direct product method,
Figure BDA0003101895070000063
wherein u isei(e)，uei(de/dt) is the current error and the level of the changed current error, respectively;
step 4-4, therefore, deriving the ith control rule from the product operation,
uΔLnRulei(ΔLs)＝sup[aiuΔL′n(ΔLs)]
wherein the membership degree is u, and the controller output is Delta LnWhile Rule is a fuzzy inference Rule as shown in FIG. 2(e), u Δ LnRulei(ΔLs) Represents the controller output Δ LsOf the ith control rule of (1), and a control decision membership, u Δ L'n(ΔLs) Output Δ L for the controllersFuzzy set Δ L 'in the domain of discourse'nOf the membership function, Δ LsAs shown in FIGS. 2(a) - (d) for the membership functions;
step 4-5, the controller outputs Delta LnIs a membership function u Δ LnIs as follows;
Figure BDA0003101895070000064
step 4-6, finally, a gravity center method is adopted to perform ambiguity resolution, and an output accurate value can be obtained in a discrete domain;
Figure BDA0003101895070000065
wherein, Δ ZsFor fuzzy control of specific output values in discrete domain, u Δ LnΔLsiIs the membership coefficient, C (Δ L) in the ith control rule on discrete domainsi) Is the corresponding membership function in the ith control rule;
4-7, multiplying the output error by the current error to obtain delta KpAnd Δ KiAnd through KuThe output of the electronic ballast is continuously adjusted,
Figure BDA0003101895070000071
Figure BDA0003101895070000072
wherein, Δ KpAnd Δ KiProportional and integral gain adjustment factors, K, of respective fuzzy outputsuTo adjust the output scale gain;
step 4-8, converting Δ KpAnd Δ KiAnd d-q axis inductance is respectively adjusted together with proportional gain and integral gain in model reference self-adaptation, so that d-q axis inductance parameters identified by the improved model reference self-adaptation can be obtained.
Figure BDA0003101895070000073
The MTPA control under the model prediction control can be realized through the steps.
The invention has the following beneficial effects:
1. the invention fully utilizes the advantages of good robustness of Fuzzy (Fuzzy) logic control, no change in system, no dependence on an accurate control model and the like. Therefore, the traditional PI controller in the adaptive law is replaced by the Fuzzy-PI controller, so that the interference of the MRAS identification parameters on the speed and load torque sudden change can be improved, and the accuracy of parameter identification can be improved.
2. The identified d-q axis parameters can obtain better control performance by replacing constant parameters in the model predictive control, enhance the robustness of the model predictive control and improve the control performance of the motor.
3. The invention uses the Fuzzy-PI regulation recognized parameters in the self-adaptive law in the formula MTPA control, can automatically find the best MTPA operating point, and obtains the minimum phase current amplitude under the same torque output, thereby reducing the copper consumption of the motor and improving the operation efficiency of the motor.
Drawings
FIG. 1: (a) a five-phase permanent magnet synchronous motor model prediction MTPA control block diagram; (b) and identifying MRAS parameters under the control of Fuzzy-PI.
FIG. 2: fuzzy logic membership functions and logic rules. (a) Membership function (b) Δ k for d-axis error and error variationp1And Δ ki1Output membership letterMembership function (d) Δ k for number (c) q-axis error and error variationp2And Δ ki2And (e) outputting the member functions and (e) fuzzy control rules.
FIG. 3: identified d-q axis inductance of the MRAS at the rotational speed step. (a) L isd(b)Lq。
FIG. 4: the identified d-q axis inductance of the MRAS at the torque step. (a) L isd(b)Lq。
FIG. 5: model predictive control indControl is compared to MTPA control at 0.
FIG. 6: the current and torque outputs predicted with and without the parameter-identified model are compared in the event of a parameter mismatch. (a) Model prediction using parameter identification and not using the overall contrast map (b) THD content without parameter identification (c) THD content using parameter identification.
Detailed Description
The invention discloses a model prediction MTPA control method for permanent magnet synchronous motor parameter identification by adopting fuzzy logic control, which comprises the following steps: detecting the rotation speed of the motor, and setting the rotation speed n*Comparing with actual feedback speed n, and obtaining motor given current I by PI controllers(ii) a And then the L is identified by utilizing the Fuzzy-PI regulation model reference self-adaptiondq(k +1) updating MTPA in real time to obtain reference current idqAnd model prediction constant parameter LdqQuilt Ldq(k +1) current i which is then output by predicting the current by a cost functionp(k +2) making a difference with the reference voltage, and selecting the magnitude of the corresponding vector as a PWM generation sequence according to the current minimum difference value; generation of phase current I of embedded permanent magnet motor by inverterabcdeAnd a position angle thetae(ii) a The phase current is changed by Park, and the phase current in a natural coordinate system is converted into the current i in a rotating coordinate systemdq(ii) a I is separated by EulerdqConverting to an input signal of a current prediction model; l for online identification of PMRAS parametersdq(k +1), selection vector, and input of discretized current prediction model to generate predicted current ip(k + 2); finally, the input signals are respectively d-q axis voltage and current u of a rotating coordinate systemdq，idqAnd rotatingA sub angular velocity; therefore, the MTPA control with the model prediction with better robustness is realized.
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
An MTPA control method for d-q axis inductance parameter identification of a permanent magnet synchronous motor using fuzzy logic control is shown in fig. 1 (a).
The specific implementation step 1:
step 1-1, a given rotating speed n of a five-phase permanent magnet motor*Comparing with feedback speed n to obtain the speed error e of motorrThe rotating speed error can be obtained by a PI controller to obtain a speed ring error signal I of the five-phase permanent magnet motors；
Step 1-2, rotating reference current i under d-q axis of coordinate systemd *、iq *Obtained from the following MTPA equation
Figure BDA0003101895070000081
Figure BDA0003101895070000082
Wherein: l ^ adq(k +1) is the on-line recognized d-q axis inductance given by the Proposed Model Reference Adaptive System (PMRAS) parameter on-line recognition;
step 1-3, predicting and outputting current i by using cost functionp(k +2) making a difference with the reference current, and selecting a corresponding vector magnitude action PWM to generate a trigger signal for driving the inverter according to the current minimum difference value after the difference is made;
step 1-4, generating S from PWMabcdeBridge arm switching sequence to drive the inverter to generate phase current I by combining the switching trigger sequence and pulse widthabcdeAnd rotor position angle thetae；
Step 1-5, the phase current is changed by Park, and the current in a natural coordinate system is converted into the current i in a rotating coordinate systemdq；
1-6, dispersing i by EulerdqConversion into input signal i controlled by predictive modelp(k+1)；
1-7, respectively using the input signals of online identification of PMRAS parameters as d-q axis voltage and current u of a rotating coordinate systemdq，idqAnd rotor angular velocity ωmIdentified L ^ sd(k +1) and L ^ Lq(k +1) updating L in model predictive control and MTPAdAnd Lq；
Step 1-8, the online identification process of PMRAS parameters is detailed in the second step;
step 1-9, predicting the current through the identified d-q axis inductance L ^d(k +1) and L ^ Lq(k +1), selecting vector Vi(i ═ 1.. 12), discrete input current ip(k +1) to generate a model predictive control current ip(k+2)；
Figure BDA0003101895070000091
Wherein id、iqIs d-q axis current, ud、uqIs d-q axis voltage, RsIs the stator resistance, Ld、LqIs a d-q axis inductor, LlsLeakage inductance, ω rotor angular velocity in the machine,. psifIs a permanent magnetic flux linkage, T is a sampling period, and k is a sampling sequence;
and 1-10, the MTPA control under model prediction control can be realized through the steps.
Application of fuzzy logic control to model reference adaptation for identification of d-q axis inductance L ^ sd(k +1) and L ^ Lq(k +1) the principle of which is shown in FIG. 1 (b). Wherein the specific fuzzy logic membership functionThe numerical and logical rules are shown in FIG. 2, where FIG. 2(a) is a membership function of d-axis error and error variation; 2(b) Δ kp1And Δ ki1A membership function of the output; 2(c) membership functions of q-axis error and error variation; 2(d) Δ kp2And Δ ki2A membership function of the output; 2(e) fuzzy control rules. Fig. 2(E) E and EC are represented by seven variables, respectively: positive large means PL, positive middle means PM, positive small means PS, zero means ZO, negative small means NS, negative middle means NM, negative large means NL.
The specific implementation step 2:
step 2-1, voltage signal VabcdeThe current signal I can be obtained by a reference modelabcdeWherein i isd、iqIs d-q axis current, ud、uqIs d-q axis voltage, RsIs the stator resistance, Ld、LqIs d-q axis inductance, omegaeIs the rotor angular velocity, psi, in the machinefIs a rotor flux linkage, and the current is converted into a current i under a rotating coordinate system under a five-phase natural coordinate system through Park conversiondq；
Reference model mathematical expression:
Figure BDA0003101895070000101
step 2-2, voltage signal VabcdeConverting the voltage under a five-phase natural coordinate system into the voltage u under a rotating coordinate system through Park conversiondqAnd used as an input signal of the adjustable model;
mathematical model of the tunable model:
Figure BDA0003101895070000102
wherein, the symbol ^ represents the parameter or signal to be identified;
step 2-3, passing the current signal i of the reference modeldAnd iqAnd the adjustable model current signal idA and iqThe difference can obtain the current errorThe output signal of the difference change e (t) contains parameter information to be identified;
Figure BDA0003101895070000103
step 2-4, multiplying the proportional gain Kc and the differential gain Ke by the current error respectively to obtain an input current error (E) and a change rate (Ec) of the fuzzy logic control, and inputting the input current error (E) and the change rate (Ec) into the fuzzy logic control, wherein the detailed process is described in the third part;
step 2-5, proportional gain K of adaptive lawpjIntegral gain Kij(j is 1, 2) through fuzzy logic control adjustment, L ^ can be identifiedd(k +1) and L ^ Lq(k +1) while feeding this value back into the adjustable model.
Figure BDA0003101895070000104
L ^ for realizing the method through the steps 1-5d(k +1) and L ^ LqAnd (k +1) identifying parameters.
As shown in fig. 3 and 4, compared with the conventional method, the parameter identification method of the present invention is closer to the reference value and has better identification accuracy; the torque and rotation speed steps in 0.1 second of fig. 3 and 4 cause disturbance to the conventional method, but the method provided by the invention has good robustness, thereby proving the correctness of the method.
As can be seen from FIG. 5, and idCompared with 0 control, the d-q axis inductance parameter of the permanent magnet synchronous motor controlled by the fuzzy logic is applied to a Formula method MTPA (FS-MTPA), and the FS-MTPA control method outputs smaller current amplitude under the conditions of given same torque and rotating speed, so that the copper consumption of the motor can be reduced, and the operation efficiency is improved.
As can be seen from fig. 6, when the model predictive control system has parameter mismatch, the parameter identification method provided by the present invention is applied to the model predictive control system to reduce current harmonic distortion (THD) and torque ripple (peak-to-peak value of torque); THD and torque ripple without parameter identification are large.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an illustrative embodiment," "an example," "a specific example," or "some examples" or the like mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims (6)

1. A MTPA control method for identifying d-q axis inductance parameters of a permanent magnet synchronous motor controlled by fuzzy logic is characterized by comprising the following steps:
step 1, firstly, deducing an MTPA current angle calculation formula of a formula method, realizing MTPA control of a permanent magnet motor when motor parameters are updated in real time, deducing a discretized model predictive control equation of the permanent magnet synchronous motor, and realizing predictive current control of the motor;
step 2, according to a Popov ultra-stability theory principle, applying the Popov ultra-stability theory principle to a model reference adaptive parameter identification method, then deducing a d-q axis inductance identification adaptive rate expression of the permanent magnet synchronous motor, and when the identified d-q axis inductance is continuously fed back to an adjustable model until the error between the identified d-q axis inductance and a reference model is almost zero, obtaining an identified d-q axis inductance parameter;
step 3, applying fuzzy logic control in model reference self-adaptation, deducing proportional and integral gain adjustment factors of a self-adaptation law, and adjusting d-q axis inductance together with proportional and integral gains in the model reference self-adaptation, so that d-q axis inductance parameters identified by the improved model reference self-adaptation can be obtained;
and 4, using the d-q axis inductance parameters of the improved model reference adaptive identification to an MTPA current angle calculation formula and prediction current control to replace original fixed parameters and update in real time, realizing MTPA control of the permanent magnet motor and eliminating the influence caused by inductance mismatch in the current prediction control, thereby improving the operating efficiency of the permanent magnet synchronous motor and increasing the parameter robustness of a control system.
2. The MTPA control method for identifying the d-q axis inductance parameter of the permanent magnet synchronous motor controlled by the fuzzy logic according to claim 1, wherein the specific process of the step 1 is as follows:
step 1-1, a given rotating speed n of a five-phase permanent magnet motor*Comparing with feedback speed n to obtain speed error e of motorrThe rotating speed error can be obtained by a PI controller to obtain a speed ring error signal I of the five-phase permanent magnet motors；
Step 1-2, rotating reference current i under d-q axis of coordinate systemd *、iq *From the following MTPA current angle equation γMTPACalculating to obtain;
Figure FDA0003101895060000011
Figure FDA0003101895060000012
wherein: l ^ ad(k+1)、L^q(k +1) identifying d-q axis inductance parameters on line respectively, which are given by the Proposed Model Reference Adaptive (PMRAS) parameter on-line identification; psifIs a permanent magnetic flux linkage parameter; i issIs the output of a rotating speed loop PI controller;
step 1-3, the cost function converts the currentPredicting the output current ip(k +2) making a difference with the reference current, and selecting a corresponding vector magnitude action PWM to generate a trigger signal for driving the inverter according to the current minimum difference value after the difference is made;
step 1-4, generating S from PWMabcdeBridge arm switching sequence to drive the inverter to generate phase current I by combining the switching trigger sequence and pulse widthabcdeAnd rotor position angle thetae；
Step 1-5, the phase current is changed by Park, and the current I under a natural coordinate system is convertedabcdeConversion into current i in a rotating coordinate systemdq；
Steps 1-6, idqInput signal i controlled by Euler discrete conversion into prediction modelp(k+1)；
1-7, respectively using the input signals of online identification of PMRAS parameters as d-q axis voltage and current u of a rotating coordinate systemdq，idqAnd rotor angular velocity ωmIdentified L ^ sd(k +1) and L ^ Lq(k +1) updating L in model predictive control and MTPAdAnd Lq；
Step 1-8, predicting the current through the identified d-q axis inductance L ^d(k +1) and L ^ Lq(k +1), selecting vector Vi(i ═ 1.. 12), discrete input current ip(k +1) to generate a model predictive control current ip(k+2)；
Figure FDA0003101895060000021
Wherein id、iqIs d-q axis current, ud、uqIs the d-q axis voltage, ix、iyIs a cubic space x-y axis current, ux、uyIs the cubic space x-y axis voltage, RsIs the stator resistance, LlsLeakage inductance, ω rotor angular velocity in the machine,. psifIs a permanent magnetic flux linkage, T is a sampling period, and k is a sampling sequence; l ^ ad(k+1)、L^q(k +1) is the on-line recognition of the d-q axis inductance parameter, respectively, by the Proposed Model Reference Adaptive (PMRAS)Identifying and giving parameters on line;
the MTPA control under the model prediction control can be realized through the steps.
3. The MTPA control method for identifying the d-q axis inductance parameter of the permanent magnet synchronous motor controlled by the fuzzy logic as claimed in claim 1, wherein the specific process of the step 2 is as follows:
step 2-1, voltage signal VabcdeThe current signal I can be obtained by a reference modelabcdeWherein i isd、iqIs d-q axis current, ud、uqIs d-q axis voltage, RsIs the stator resistance, Ld、LqIs d-q axis inductance, omegamIs the rotor angular velocity, psi, in the machinefIs a rotor flux linkage, and the current is converted into a current i under a rotating coordinate system under a five-phase natural coordinate system through Park conversiondq；
The model reference adaptive reference model equation is expressed as:
Figure FDA0003101895060000031
step 2-2, voltage signal VabcdeConverting the voltage under a five-phase natural coordinate system into the voltage u under a rotating coordinate system through Park conversiondqAnd used as an input signal of the adjustable model;
the model reference adaptive adjustable model equation is expressed as:
Figure FDA0003101895060000032
wherein, the symbol ^ represents the parameter or signal to be identified;
step 2-3, passing the current signal i of the reference modeldAnd iqAnd the adjustable model current signal idA and iqSubtracting to obtain the output signal of the current error variation e (t) and containing the requirementIdentified parameter information;
Figure FDA0003101895060000033
4. the MTPA control method for identifying the d-q axis inductance parameter of the permanent magnet synchronous motor controlled by the fuzzy logic as claimed in claim 3, wherein the specific process of the step 3 is as follows:
step 3-1, in order to describe the equations briefly, the reference model formula of step 2-1 and the adjustable model formula of step 2-2 are rewritten with the following standard form:
Figure FDA0003101895060000034
wherein the content of the first and second substances,
Figure FDA0003101895060000035
the symbol ". quadrature" represents a differential operator, an adjustable model is established according to a standard form, all parameters are completely consistent with a reference model except d-q axis inductance and current, and an identification value is represented by a symbol "^ quadrature"; in the formula udqIs the stator shaft voltage, idqIs stator shaft current, RsIs the stator phase resistance, omegaeIs the electrical angular velocity, #fIs a motor permanent magnet flux linkage;
step 3-2, the adjustable model is rewritten as follows:
Figure FDA0003101895060000036
wherein the content of the first and second substances,
Figure FDA0003101895060000041
step 3-3, according to the principle of Popov ultra-stability theory, if the feedback system needs to be kept stable, the nonlinear loop should satisfy the following formula
Figure FDA0003101895060000042
And is provided with
Figure FDA0003101895060000043
Where η (0, t) is an integral function, r2Is a finite normal quantity independent of the upper integral limit t, W is a nonlinear feedback input, W1 is an intermediate variable and W1 is-W;
the design purpose of the parameter self-adaptive law is to estimate the needed parameters on line, and then the generalized error of the control system gradually tends to zero through feedback regulation;
firstly, analyzing the self-adaptive law of direct axis inductance identification, and providing a corresponding calculation formula, wherein the derivation process of the quadrature axis self-adaptive law is similar to that of the direct axis self-adaptive law, and is omitted;
Figure FDA0003101895060000044
wherein, L ^ sdFor the direct-axis inductance parameter to be identified, LdFor reference to the direct-axis inductance parameter, R1(τ) is an integral function with respect to τ, R2(τ) is a function of τ;
then, the above formula is substituted into the equation of step 3-3 to obtain
Figure FDA0003101895060000045
Wherein the symbol ". quadrature" represents a difference operator, ε is a current output difference between a reference model and an adjustable model, ε1For the output of the direct-axis current of the reference model and the adjustable modelA difference;
the above formula is solved by the following theorem,
Figure FDA0003101895060000046
and a positive real constant k is greater than 0, f (t) is a multiplicative function with respect to t,
is given the following formula
Figure FDA0003101895060000047
Then R can be obtained1(τ)＝Ki1e1u, as long as Kp1Greater than 0, then R2(τ)＝Kp1ε1udThen the adaptation is as follows:
Figure FDA0003101895060000051
wherein, Kp1、Ki1Respectively, the direct axis inductance parameter L ^dProportional integral gain of adaptive rate, Kp2、Ki2Respectively is a quadrature axis inductance parameter L ^qProportional integral gain of adaptation rate.
5. The MTPA control method for identifying d-q axis inductance parameters of the permanent magnet synchronous motor controlled by the fuzzy logic as claimed in claim 4, wherein the design of the adaptive law adopts a proportional-integral regulation principle, and in order to satisfy the requirement of the model reference adaptive control system to be hyperstable according to the hyperstable law, not only the strict and positive implementation of a linear constant forward channel is required to be satisfied, but also a nonlinear feedback loop satisfies an integral inequality, so that a series of derivation processes are omitted, and an adaptive law expression of the motor parameters is obtained.
6. The MTPA control method for identifying the d-q axis inductance parameter of the permanent magnet synchronous motor controlled by the fuzzy logic as claimed in claim 1, wherein the specific process of the step 4 is as follows:
step 4-1, passing the current signal i of the reference modeldAnd iqAnd the adjustable model current signal idA and iqSubtracting to obtain the output signal of the current error change e (t), and the output signal contains the parameter information to be identified;
Figure FDA0003101895060000052
step 4-2, multiplying the proportional gain Kc and the differential gain Ke by the current error respectively to obtain an input current error E of the fuzzy logic control and a change rate Ec thereof, and then inputting the input current error E and the change rate Ec into the fuzzy logic control;
step 4-3, resolving the fuzzy, converting the implicit fuzzy control set into explicit output through a gravity-center ambiguity resolving technology, wherein the fuzzy reasoning adopts a direct product method,
Figure FDA0003101895060000053
wherein u isei(e)，uei(de/dt) is the current error and the level of the changed current error, respectively;
step 4-4, therefore, deriving the ith control rule from the product operation,
uΔLnRulei(ΔLs)＝sup[aiuΔL′n(ΔLs)]
wherein the membership degree is u, and the controller output is Delta LnRule is a fuzzy inference Rule, u Δ LnRulei(ΔLs) Represents the controller output Δ LsOf the ith control rule of (1), and a control decision membership, u Δ L'n(ΔLs) Output Δ L for the controllersFuzzy set Δ L 'in the domain of discourse'nOf the membership function, Δ LsIs a membership function;
step 4-5, the controller outputs Delta LnIs a membership function u Δ LnIs as follows;
Figure FDA0003101895060000061
step 4-6, finally, a gravity center method is adopted to perform ambiguity resolution, and an output accurate value can be obtained in a discrete domain;
Figure FDA0003101895060000062
wherein, Δ ZsFor fuzzy control of specific output values in discrete domain, u Δ LnΔLsiIs the membership coefficient, C (Δ L) in the ith control rule on discrete domainsi) Is the corresponding membership function in the ith control rule;
4-7, multiplying the output error by the current error to obtain delta KpAnd Δ KiAnd through KuThe output of the electronic ballast is continuously adjusted,
Figure FDA0003101895060000063
Figure FDA0003101895060000064
wherein, Δ KpAnd Δ KiProportional and integral gain adjustment factors, K, of respective fuzzy outputsuTo adjust the output scale gain;
step 4-8, converting Δ KpAnd Δ KiAnd d-q axis inductance is respectively adjusted together with proportional gain and integral gain in model reference self-adaptation, so that d-q axis inductance parameters identified by the improved model reference self-adaptation can be obtained.
Figure FDA0003101895060000065
Wherein, Kp1、Ki1Respectively, the direct axis inductance parameter L ^dProportional integral gain of adaptive rate, Kp2、Ki2Respectively is a quadrature axis inductance parameter L ^qProportional integral gain of adaptation rate.
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