KR20080061576A - The control device of motor and the method using the adaptation integral binary monitor and fuzzy logic controller - Google Patents

Abstract

A control device of a motor, and a controlling method using an adaptation integral binary monitor and a fuzzy logic controller are provided to perform speed sensorless control of an IPMSM(Interior Permanent Magnet Synchronous Motor) without variation in a PI(Proportional Integral) gain value. A control device(100) of a motor using an adaptation integral binary monitor(170) and a fuzzy logic controller(160) includes a speed indicator(110), a speed controller(120), a current controller(130), a power converter(140), a current detector(150), and the adaptation integral binary monitor. The control device of the motor includes a fuzzification member(161), a regulation based setting member(162), a non-fuzzification member(163), and the fuzzy logic controller. The regulation based setting member extracts a fuzzy control result for a fuzzificated input. The non-fuzzification member performs a decoding operation of expressing information outputted through the regulation based setting member as specific values. The fuzzy logic controller determines an error variation rate and an actual speed after calculating an error between an indication speed and an estimation speed through comparison.

Description

The control device of motor and the method using the adaptation integral binary monitor and fuzzy logic controller}

1 is a block diagram showing the configuration of a permanent magnet synchronous motor control apparatus using a conventional adaptive integral binary observer,

2 is a block diagram showing a motor vector control according to an embodiment of the present invention,

3 is a schematic block diagram showing an electric motor control apparatus according to an embodiment of the present invention;

4 is a block diagram illustrating a binary observer having a switching plane in which an integral term is added according to an embodiment of the present invention;

5 is a diagram illustrating a state trace of a binary observer having an integrated switch plane;

6A is a triangular waveform diagram illustrating a membership function relating to a speed error according to an embodiment of the present invention;

6B is a triangular waveform diagram illustrating a membership function relating to an error change according to an embodiment of the present invention;

6C is a triangular waveform diagram illustrating a membership function relating to an output according to an embodiment of the present invention;

7A is an overall flowchart illustrating a control method using an electric motor control apparatus according to an embodiment of the present invention;

7b is a detailed flowchart illustrating an estimated speed calculation step according to an embodiment of the present invention;

8A is a waveform diagram showing an actual speed (up) and an estimated speed (down) when driving from no load 2000 [rpm] to -2000 [rpm] when having a fixed PI gain;

8B is a waveform diagram showing an actual speed (upper) and an estimated speed (lower) at 2000 [rpm] when a load of 70% is applied when having a fixed PI gain;

8C is a waveform diagram showing an actual speed (up) and an estimated speed (down) at 1000 [rpm] when a load of 100% is applied when having a fixed PI gain;

8D is a waveform diagram showing an actual speed (upper) and an estimated speed (lower) when driving at no load 50 [rpm] when having a fixed PI gain;

8E is a waveform diagram showing an actual position (upper) and an estimated position (lower) when driving from 50 [rpm] to -50 [rpm] when no load is applied when having a fixed PI gain.

*** Explanation of symbols for the main parts of the drawing ***

110: speed commander 120: speed controller

130 current controller 140 power converter

150: current detector 160: fuzzy logic controller

161: fuzzy step 162: rule-based setting means

163 non-fuzzy means 170: adaptive integral binary observer

171: first adder 172: integrator

173: second adder 174: auxiliary loop regulator

175: main loop regulator 176: gain regulator

177: function input method

The present invention relates to a method for controlling the sensing of an embedded permanent magnet synchronous motor (IPMSM) using an adaptive integral binary observer and a fuzzy logic controller, and more specifically, by using a fuzzy logic controller when a command speed or load changes. The present invention relates to an electric motor controller using an adaptive integral binary observer and a fuzzy logic controller capable of speed sensing control of an IPMSM without a gain value change.

On the other hand, a number of technologies related to the binary observer, including the Republic of Korea patent registration (No. 10-0551572, permanent magnet synchronous motor control device using an adaptive integral binary observer and its control method) (hereinafter referred to as "prior invention") It is filed and registered.

As described above, the preceding invention outputs a current controller between a speed controller for outputting a current command value to follow the command speed output from the speed commander, and an actual current flowing through the current command value and the brushless DC motor. A current controller configured to determine a switching pattern for the current error and supply a voltage according to the determined pattern to a DC motor, a current detector for sensing a current flowing in the DC motor, and a rotor of the DC motor. A motor control apparatus comprising a binary observer for estimating a speed and a position of a binary observer, wherein the binary observer has an integral switching plane that estimates the speed and position of the rotor of the DC motor by integrating the current error. Is the real current flowing through the DC motor and the state equation A first adder for obtaining an estimated error between the obtained estimated current, an integrator for integrating an estimated error of the first adder, a second adder for adding an output of the integrator and an estimated error of the first adder, and the second adder An auxiliary loop adjuster for setting a limit region of the allowable range for the output of the adder, and setting the gain of the main loop adjuster so that the estimated error of the first adder stays within the region without leaving this region; A main loop adjuster for outputting a continuous switching function using the output of the auxiliary loop adjuster and the estimated error of the first adder, and a gain of the continuous switching function outputted from the main loop adjuster within the limit region. It is a configuration that includes a gain adjuster that allows the incoming error trajectory to converge stably to the origin. There is a problem that a speed estimation error exists because an error occurs in measurement of a synchronous parameter and an error occurs in driving current or input voltage detection.

That is, there is a problem that a speed estimation error exists due to an error that occurs when measuring motor parameters and an error that occurs when the current or input voltage is detected when the motor is driven.

In other words, the conventional IPMSM had to change the PI gain of the PI controller according to the speed or the load according to the change of the command speed or the load.

Therefore, if the PI gain is fixed and then driven by changing the command speed of the motor, there is a problem that the motor is not driven at a certain speed range.

The present invention has been made to solve the above-mentioned problems, and by using a fuzzy logic controller when a command speed change or load change, an adaptive integral binary observer and a fuzzy control capable of speed sensing control of IPMSM without a PI gain value change A motor control apparatus using a logic controller and a method thereof are provided.

In order to realize such a technical idea, the speed commander 110 outputs a command speed separately designated by a user, and the speed controller 120 outputs a current command value to follow the command speed output from the speed commander. And a current controller 130 for outputting a voltage to follow the current command value output from the speed controller, and a power converter for converting the voltage output from the current controller into a three-phase AC power source and supplying the embedded permanent magnet synchronous motor. 140 and the current detector 150 for sensing the current flowing through the IPMSM, and after estimating the speed and position of the motor rotor using the current detected by the current detector, the estimated speed is SVPWM to the IPMSM through the inverter. Adaptive integral consisting of an adaptive integral binary observer 170 which is an estimator provided to the current controller. In the motor controller 100 using a binary observer and a fuzzy logic controller, the motor controller 100 is a new fuzzy value that can be handled by the fuzzy logic controller through an appropriate return function according to the speed deviation and the change of the speed deviation. Fuzzy means 161 for converting to an information value, rule-based setting means 162 for deriving a result of fuzzy control on the fuzzy control input, and information output through the rule-based setting means. Non-fuzzy means 163 for performing a decoding operation represented by a numerical value, and compares the error between the command speed and the estimated speed, calculates the errors that occur between them, and then determines the rate of change of the error and the actual speed Fuzzy logic controller 160 to perform the; It includes.

Preferably, the rule-based setting means 162 quantifies the calculation through a minimum operator calculation method or a scalar product operation method in order to derive the result of the fuzzy control.

Preferably, the non-fuge means 163 is characterized by obtaining a definite value for the degree of output (purge value) by using a center of gravity (COG) method.

On the other hand, the present invention provides an electric motor control method using an adaptive integral binary observer and a fuzzy logic controller, comprising: a first step of calculating an estimated speed when a variable load or a change in command speed occurs; A second process of outputting a first command current through the power converter 140; A third step of calculating an error current between the first command current and the actual current; A fourth process of calculating a second command current according to the error current through a PI controller; A fifth process of acquiring a final command current; A sixth step of calculating a command voltage using the final command current by a rotor voltage method of IPMSM; And a seventh step of determining a continuous switching function having an integral switching plane; The second process may include: obtaining a speed error by comparing a command speed and an estimated speed; Obtaining a rate of change of the speed error by comparing the current speed error with the previous step speed error; Calculating a FLC current using the rate of change of the speed error; And obtaining a first command current through the fixed PI controller using the speed error change rate. Characterized in that it comprises a.

Preferably, before the first step, the step of outputting a command speed separately designated by the user; Measuring a real current applied to the IPMSM; Calculating an estimated speed from the state equation of the IPMSM; It characterized in that it further comprises.

Also preferably, after the second process, passing the PI controller to calculate the error between the first command current and the actual current; It characterized in that it further comprises.

And preferably, the process of acquiring the final command current is characterized in that the final estimated current is obtained by adding the result calculated from the fuzzy logic controller to the value obtained by the fixed PI gain.

The features and advantages of the present invention will become more apparent from the following detailed description based on the accompanying drawings. In the meantime, when it is determined that the detailed description of the known functions and configurations related to the present invention may unnecessarily obscure the subject matter of the present invention, it should be noted that the detailed description is omitted.

Hereinafter, referring to FIG. 2 to FIG. 6C, a motor control apparatus (hereinafter, referred to as an “motor controller”) using an adaptive integral binary observer and a fuzzy logic controller according to an embodiment of the present invention will be described with reference to the accompanying drawings. The description is as follows.

Figure 2 is a block diagram showing a motor vector control according to an embodiment of the present invention, Figure 3 is a schematic block diagram showing a motor control apparatus according to an embodiment of the present invention, Figure 4 is an embodiment of the present invention FIG. 5 is a block diagram illustrating a binary observer having a switching plane in which an integral term is added, and FIG. 5 is a view illustrating a state trace of a binary observer having an integrated switch plane, and FIG. 6A illustrates a velocity according to an embodiment of the present invention. 6B is a triangular waveform diagram illustrating a membership function relating to an error, and FIG. 6B is a triangular waveform diagram illustrating a membership function relating to an error change according to an embodiment of the present invention, and FIG. 6C is a diagram illustrating an output according to an embodiment of the present disclosure. Triangle waveform diagram showing membership function.

Referring to the motor control device with reference to FIGS. 2 and 3, the motor control device 100 includes a speed commander 110 for outputting a command speed separately designated by a user, and a command speed output from the speed commander 110. The speed controller 120 outputs a current command value to follow the current, the current controller 130 outputs a voltage to follow the current command value output from the speed controller 120, and the voltage output from the current controller 130 is 3 A power converter 140 converting into a phase alternating current power supply to an embedded permanent magnet synchronous motor (hereinafter referred to as IPMSM), a current detector 150 for sensing current flowing through the IPMSM, and And a fuzzy logic controller (FLC) 160 for determining the rate of change of the error and the actual speed after comparing the error between the command speed and the estimated speed, and calculating the error occurring therebetween. By estimating the speed and position of the motor rotor using the current detected at 150, the estimated speed is changed to the voltage value by the voltage equation after comparing with the command speed, and compared with the actual current, and SVPWM Applied to the IPMSM, and the estimated position includes an adaptive integral binary observer 170 that is an estimator provided to the current controller 130.

At this time, the adaptive integral binary observer 170 detects the a phase current and the b phase current applied to the IPMSM, and the c phase current is preferably calculated.

Here, the power converter 140 converts the a, b and c phase currents into the stator coordinate system phase D-Q.

Hereinafter, the technical spirit of the present invention will be described based on a number of equations.

First, the basic voltage equation according to the stator coordinate system α-β of the IPMSM will be described as Equation 1 below.

를 나타내는 것이 바람직하다.Where i _s represents the stator α and β axis currents as [i _α i _β ] ^T , v _s represents the stator α and β axis voltages as [v _α v _β ] ^T , R represents the stator, L _d represents the rotor d-axis inductance, L _q represents the rotor q-axis inductance, K _E represents the counter electromotive force constant, ω represents the rotor speed, θ represents the rotor position,

It is preferable to represent.

On the other hand, if the above-described equation (1) is expressed as a differential equation, the following equation (2) is obtained.

를 나타내며,

를 나타내고,

를 나타내며,

를 나타내는 것이 바람직하다.Where E _s represents the stator α and β axis induced voltages as [E _α E _β ], E _α represents K _E ωsinθ, E _β = -K _E ωcosθ],

Indicates

Indicates,

Indicates

It is preferable to represent.

That is, the entire system becomes nonlinear due to the counter electromotive force component represented by multiplying the trigonometric function and velocity terms for voltage, current, and position, which are variables of time, by using Equation 2, thereby using the adaptive integral binary observer 170. . In other words, in order to linearize the nonlinear system, observation is made on a current that can be measured under the assumption that the velocity is constant within a control period. The adaptive integral binary observer 170 is expressed by Equation 3 below. .

인 상수를 나타내고,

는 고정자 α축과 β축 추정전류를 나타내며,

는 고정자 α축과 β축 추정유도전압을 나타내고,

를,

를

를,

은 관측기 입력을 나타낸다.At this time,

Represents a constant

Represents the stator α- and β-axis estimated currents,

Represents the estimated induction voltages of the stator α and β axes,

To,

To

To,

Represents the observer input.

Meanwhile, in the present embodiment, the above-described binary observer is set as a binary observer 170 having an integral switch plane (hereinafter referred to as an adaptive integral binary observer), which is a conventional binary observer whose origin is the plane of the observer. It is not possible to guarantee convergence.

That is, although it is possible to reduce the trembling of the estimated value in the steady state, it is preferable to use the binary observer 170 having an integral switching plane to solve the above-mentioned problem because it has the disadvantage that the steady state error may remain.

In this way, the integral term is added to the hyperplane to reduce the error in the steady state, and the hyperplane of the constructed observer is defined as the error between the estimated current and the actual current.

The adaptive integral binary observer 170 will be described in detail with reference to FIG. 4.

)간의 추정오차(e_s)를 산출하는 제 1가산기(171)와, 상기 제 1가산기(171)의 출력값인 추정오차(e_s)를 적분하는 적분기(172)와, 적분기(172)의 출력과 제 1가산기(171)의 출력을 가산하는 제 2가산기(173)와, 제 2가산기(173)의 출력값(σ(t))에 대한 허용범위의 한계영역(G_δ)을 설정하고, 제 1가산기(171)의 출력(e_s)이 설정된 한계영역에 들어오면 이 영역을 벗어나지 않고 계속 그 영역 내에 머물러 있도록 주루프 조정기의 이득(K₁) 을 설정하는 보조루프 조정기(174)와, 보조루프 조정기(174)의 출력과 제 1 가산기(171)의 출력(e_s)을 입력으로하여 연속의 스위칭함수를 출력하는 주루프 조정기(175)와, 주루프 조정기(175)에서 출력되는 연속의 스위칭함수의 이득을 조정하여 한계영역내에 들어온 오차궤적이 원점으로 안정적으로 수렴할 수 있도록 하는 이득조정기(176) 및 IPMSM의 회전자 속도를 추정하고 안정도를 판별하기 위하여 리아푸노프(Lyapunov)함수를 입력하는 함수 입력기(177)를 포함한다.As shown in FIG. 4, the adaptive integral binary observer 170 estimates the actual current (i _s ) flowing in the IPMSM and the estimated current (obtained from the state equation).

) Output of the first adder 171 and the first adder of the estimated output value of 171, the error (e _s) the integrator 172 and an integrator 172 for integrating the calculating the estimated error (e _s) between And the second adder 173 for adding the output of the first adder 171, and the limit region G _δ of the allowable range for the output value σ (t) of the second adder 173, and When the output e _s of the adder 171 enters the set limit region, the auxiliary loop adjuster 174 sets the gain K ₁ of the main loop adjuster so as to remain within the region without leaving this region, and the auxiliary loop adjuster 174. A main loop regulator 175 for outputting a continuous switching function by inputting the output of the loop regulator 174 and the output e _s of the first adder 171, and a continuous output from the main loop regulator 175 A gain adjuster 176 that adjusts the gain of the switching function so that the error trajectory within the limit region can converge stably to the origin. And a function input unit 177 for inputting a Lyapunov function to estimate the rotor speed of the IPMSM and to determine the stability.

) 및 추정속도(

)를 입력받아 일반적인 바이너리 관측기에 적분항을 추가한 스위칭 평면을 갖는다.In this case, the adaptive integral binary observer 170 estimates a position through the Lyarunov function.

) And estimated speed (

) Has a switching plane that adds an integral term to a typical binary observer.

Therefore, the integral switching plane σ (t) measured by the binary observer 170 having the integral switching plane is expressed by Equation 4 below.

이고,

이며,

이고,

를 나타내는 것이 바람직하다.Where C _α and C _β represent positive constants

ego,

Is,

ego,

It is preferable to represent.

Next, the hyperplane through the above-described equation (4) to increase the dimension of the hyperplane by integrating the error (e) of the current calculated through the first adder 171, as shown in FIG. State trajectories can be represented in the plane.

That is, the limit region G _δ defined in the binary observer is expressed by Equation 5 below.

G _δ = x: δ ⁺ δ ^- ≤ 0

In this case, δ = a ⁺ δ (t) -c · δ, δ - = δ (t) c · If, 0≤δ <1 when δ δ represents a constant.

In other words, it can be seen that the dimension of the adaptive integral binary observer 170 is increased by converging the region of the adaptive integral binary observer 170 to the origin using Equation 4 above.

)와 실제전류(i_s)와의 오차(e)를 적분항을 추가하여 적응 적분 바이너리 관측기(170)의 초평면을 구성하는 것을 볼 수 있다.For reference, the estimated current (

It can be seen that by constructing the hyperplane of the adaptive integral binary observer 170 by adding the integral term to the error (e) between the actual current i _s ).

Meanwhile, the main loop adjuster 174 and the auxiliary loop adjuster 175 for determining the switching function of the binary observer 170 having the integral switching plane are continuous inertia (COFB), which are expressed as Equations 6 and 7 below. do.

First, the auxiliary loop adjuster 171 is as shown in Equation 6 below.

The main loop adjuster 175 is expressed by Equation 7 below.

이고,

인 것이 바람직하다.At this time,

ego,

Is preferably.

In addition, the error equation of the binary observer 170 having an integral switching plane is summarized as in Equation 8 below by using Equations 6 and 7 described above.

That is, the error equation of the binary observer 170 having the integral switching plane can be estimated using the auxiliary loop adjuster 174 and the main loop adjuster 175.

을 나타내는 바, 도 5를 참고하여 적분 스위칭 평면을 갖는 바이너리 관측기(170)의 상태궤적을 살펴보면, 오차는 바이너리 관측기의 경계면에 도달한 후, e_s=0이 될 때까지 수평축을 따라 수렴해간 다.At this time,

Referring to FIG. 5, the state trace of the binary observer 170 having an integral switching plane, after reaching the boundary of the binary observer, converges along the horizontal axis until e _s = 0. .

Next, the gain K ₁ of the binary observer with integral switching plane can be obtained from the invariant condition for the limit region G _δ .

In this case, the region invariant condition must satisfy the following Equation 9 at the boundary of the limit region G _δ .

First, the value of gain K ₁ is set with reference to Equation 9 described above.

That is, the condition that δ (t) entered in the limit region G _δ does not leave the region and remains in the limit region G _δ can be ensured by appropriately selecting the gain K _α . First, consider the case where δ> 0.

Therefore, assuming that δ _α > 0, the gain K _α is selected so as to satisfy the above equation (9).

On the other hand, from the above equation (10), the value of the gain K _α can be expressed as shown in the following equation (11).

At this time, when δ _α <0, the following Equation 12 can be obtained.

Thus, Equation 13 can be calculated using Equations 11 and 12 described above.

On the other hand, by using the above-described method, the following equation (14) can be calculated for δ _β .

In addition, equation (15) can be calculated using the above equations (13) and (14).

Next, the auxiliary loop adjuster gain α is obtained by using a function λ = δσ as a gain such that μ (t) satisfies the magnitude of μ 1-h at the boundary of the region. First, when the state of the system passes σ = 0 and the time to reach the boundary of the region at t ₁ , δ> 0 is t ₂ , and the time is summarized using Equation 6, .

At this time, when t> t ₀ , t ₁ represents the time when λ = 1/2, and t ₂ represents the time when λ = 1. However, 1/2 <h <1.

That is, α to be μ≤ (1-h) for σ> 0 can be obtained as follows. In this case, assuming that μ (t ₂ )>-(1-h), and λ (t) is investigated from t ₁ to t ₂ , the following equation (17) is obtained.

Where t≥ t ₀ ,

It is assumed that the secondary loop adjuster gain α of the observer satisfies the inequality as shown in Equation 16 described above.

Therefore, by using Equation 16, Equation 17 is summarized, and Substituting Equation 18 here results in Equation 19 below.

In other words, the magnitude of λ should be λ (t ₂ ) = 1 at the boundary of the region, that is, t ₂ as described above, and λ (t ₂ ) <1 becomes a contradiction, where α is expressed by Equation 18 Is set to be satisfied, the relationship of μμ (t ₂ ) ≥ 1-h is always established.

Also in the case of δ <0, the same result as in Equation 18 can be obtained.

On the other hand, since the adaptive integral binary observer 170 does not use a motion equation that includes a mechanical power factor, in order to obtain speed and position information of the IPMSM rotor, an estimation equation for speed and position is required.

Therefore, in the present embodiment, the Lyapunov function is used to estimate the rotor speed and determine the stability of the IPMSM.

That is, the Liafuno function is set through the function input unit 177 as shown in Equation 20 below.

In other words, after assuming that the speed of the IPMSM is constant within one estimation period, the derivative of Equation 20 can be obtained, as shown in Equation 21 below.

On the other hand, in order to estimate the rotor speed and determine the stability, by substituting Equation 8 into Equation 21, the derivative value of the Liapunov function can be expressed as Equation 22 below.

이다.At this time,

to be.

을 만족해야 한다. 따라서

을 만족하도록 하기 위하여 상기 수학식 22로부터 다음의 수학식 23 및 24로 두개의 식으로 분리한다.In addition, when the system of the adaptive integral binary observer 170 is stable, when V> 0 from the Liapunov stability theory,

Must be satisfied. therefore

In order to satisfy the following equations, the equations (22) are separated into the following equations (23) and (24).

That is, when Equation 23 is set to '0' to derive the estimated speed of the rotor, and the inequality of Equation 24 is set to determine stability, the Liapunov function of Equation 20 is It becomes stable.

In addition, the range of K ₁ from the inequality of Equation 24 is equal to Equation 25, and in this case, since K ₁ must be set to satisfy both Equations 25 and 15, the condition of Equation 26 below must be satisfied.

For reference, the equation 23 can be expressed as follows. It can be seen that the speed of the IPMSM is related to the information on the counter electromotive force, the current, and the difference between L _d and L _q .

라 근사하여 정리하면, 다음과 같이 회전자의 속도 추정식으로 수학식 28을 얻을 수 있다.In Equation 27

In summary, Equation 28 can be obtained from the rotor speed estimation equation as follows.

It can be seen that the speed of the rotor can be estimated using Equation 28. In order to quickly and stably converge the estimated speed to the actual speed, Equation 28 is proportionally integrated to determine the estimated speed. Calculate the estimated position.

Then, the error between the command speed and the estimated speed is calculated using the fuzzy logic controller 160 to calculate an error occurring therebetween, and then the rate of change of the error and the actual speed are determined.

In this embodiment, the rate of change of the speed deviation and the speed deviation is preferably expressed by Δω (e) and Δe.

The purge logic controller 160 has a purge means 161 for converting the fuzzy logic controller 160 into a new fuzzy value (information value) that can be handled by the fuzzy logic controller 160 according to the speed deviation and the change of the speed deviation. Rule-based setting means 162 and defuzzy means 163.

 Looking at the fuzzy means 161 in detail, it performs the step of coding the input of the fuzzy logic controller to a value between 0 and 1, the input x and y to the appropriate range for the conversion to the fuzzy value Language variables are defined by language descriptions such as “very large” and “little”. That is, language variables can be subdivided into various ways depending on how sophisticated the fuzzy logic controller is intended to be designed.

For reference, look at the generally applied language variables through Table 1 can be seen that it is divided into seven steps.

Language description Language variables Application value very big  PL (Positive Large) 3 Big PM (Positive Middle) 2 A little big PS (Positive Small) One It is suitable    ZE (Zero) 0 A little small  NS (Negative Small) -One small NM (Negative Middle) -2 Very small NL (Negative Large) -3

In this embodiment, language variables are set to 3, 2,... For the operation of the fuzzy logic controller. It can also be set to integer values up to -3.

On the other hand, the application value according to the language variable divided as shown in Table 1 may not be defined as it is.

For example, referring to Figs. 5A to 5B, each ear velocity function is divided into five stages of speed deviation and three stages of change rate of the speed deviation, and the applied value according to the rate of change of the speed deviation and the speed deviation is -25 to 25. The control value may be defined as -1 to 1.

In addition, the rule-based setting means 162 sets the rule base by the selection determined to be appropriate according to the user's experience and knowledge, and performs a function of deriving an appropriate fuzzy control result for the fuzzy input.

For example, the rule base of applying the language variables of Table 1 to two inputs x and y is as follows.

Same as “If x is PL and y is PS, then u is NL”, in which case the If clause is called the predecessor and the then clause is the aftergeon. This rule base can be summarized as shown in Table 2 below.

i _q △ ω (e)  △ e NL NS Z PS PL NE VH H H M L ZE VH H M L VL PE H M L L VL

For reference, a minimum operator (MIN) or a scalar product is mainly used to quantify the linguistic " and " operation of the construction unit, but in this embodiment, it is preferable to use the MIN operation.

In addition, the non-fuzzy means 163 may be referred to as a kind of decoding expressing the information output through the rule-based setting means 162 in a specific numerical value.

That is, as a method of obtaining a definite value for the degree of output (purge value) by using a center of gravity (COG), it is expressed by Equation 29 below.

Where u ^crisp is the final value of the output, b _i is the center of the velocity function representing the magnitude of the output for rule i and μ _(i) is the area of the magnitude of the output.

Incidentally, in the present invention, the degree of output μ _(i) corresponds to i _f , and the relation thereof is as shown in Equation 30, where the sum of the estimated current i ^* _q and the actual current i _q is added to the proportional integral value. It is desirable to lose.

Hereinafter, a motor control method using an adaptive integral binary observer and a fuzzy logic controller through the application software having the above-described configuration will be described with reference to FIGS. 7A to 8E.

7A is an overall flowchart illustrating a control method using an electric motor control apparatus according to an embodiment of the present invention, FIG. 7B is a detailed flowchart illustrating an estimated speed calculating step according to an embodiment of the present invention, and FIG. 8A is fixed. In case of having PI gain, it is a waveform diagram showing actual speed (upper) and estimated speed (lower) when driving from no load 2000 [rpm] to -2000 [rpm], and FIG. 8B is 70% when having fixed PI gain Is a waveform diagram showing the actual speed (upper) and estimated speed (lower) at 2000 [rpm] when applying a load of FIG. 8C is 1000 [rpm when applying a load of 100% in the case of having a fixed PI gain. ] Is a waveform diagram showing the actual speed (upper) and the estimated speed (lower), and FIG. 8D shows the actual speed (upper) and the estimated speed (lower) when driving at no load 50 [rpm] when having a fixed PI gain. 8E is a waveform diagram showing, in the case of having a fixed PI gain, no load is applied. This is a waveform diagram showing the actual position (upper) and estimated position (lower) when driving from 50 [rpm] to -50 [rpm].

First, as illustrated in FIG. 7A, the current controller 130 calculates an error between the current command value transmitted from the speed controller 120 and the actual current of the IPMSM detected through the current detector 150 (S2). Output to the converter 140 (S4).

At this time, in order to perform the step S2, the motor control apparatus 100 refers to FIG. 7B to measure the actual current applied to the IPMSM (S2a), and calculates an estimated speed from the state equation of the IPMSM (S2b). .

Next, the adaptive integral binary observer 170 compares the command speed and the estimated speed separately designated by the user (S2c) to obtain a speed error (S2d). At this time, the fuzzy logic controller 160 obtains the rate of change of the speed error by comparing the current speed error with the previous step speed error (S2e).

That is, the fuzzy logic controller 160 calculates the FLC current by using the current speed error and the change of the speed error (S2f).

The motor controller 100 obtains the first command current through the fixed PI controller (S2g).

That is, the first command current obtained by the speed error is passed to the PI controller to calculate an error from the actual current.

For reference, a phase current and b phase current applied to IPMSM are detected, and c phase current is calculated.

In addition, the power converter 140 converts a, b, and c phase currents into a stator coordinate system (D-Q).

Next, the motor control apparatus 100 calculates an error between the first command current and the actual current (S6).

The motor control apparatus 100 obtains the second command current from the error current calculated through the step S6 through the fixed PI controller (S8).

The motor control apparatus 100 obtains a final command current by adding the second command current and the FLC current (S10).

In addition, the motor control apparatus 100 calculates the command voltage by the rotor voltage method of the IPMSM (S12).

Next, the motor controller 100 determines a continuous switching function having an integral switching plane (S14).

Finally, the motor control apparatus 100 is converted from the rotor coordinate system to the stator coordinate system and then SVPWM the q-axis command voltage to the IPMSM through the inverter (S16).

In other words, the IP controller may be driven using the fuzzy logic controller 160 without adjusting the PI gain to the speed or the load according to the change of the command speed or the load according to the IPMSM.

For example, FIGS. 8A to 8E illustrate a case in which the IPMSM is driven in a fixed state without modifying the PI gain by using the fuzzy logic controller 160 according to the estimated speed or the load change of the IPMSM.

Specifically, FIG. 8A is a waveform diagram illustrating an actual speed (up) and an estimated speed (down) when driving from no load 2000 [rpm] to -2000 [rpm] when having a fixed PI gain.

8B is a waveform diagram showing an actual speed (upper) and an estimated speed (lower) at 2000 [rpm] when a load of 70% is applied when a fixed PI gain is applied, and FIG. 8C is a fixed PI gain. Is a waveform diagram showing the actual speed (upper) and estimated speed (lower) at 1000 [rpm] when 100% of the load is applied, and FIG. 8D shows a no-load 50 [rpm] when the fixed PI gain is applied. Figure 8e is a waveform diagram showing the actual speed (upper) and the estimated speed (lower) when driving, Figure 8e shows the actual speed when driving from 50 [rpm] to -50 [rpm] when no load is applied, when there is a fixed PI gain It is a waveform diagram which shows a position (upper) and an estimated position (lower).

As described above, according to the present invention, by using the fuzzy logic controller according to the estimated speed of the IPMSM or the change of the load, there is a peculiar effect of not having to modify the PI gain.

In other words, in actual driving, it is possible to change the load or change the command speed without stopping the motor system, so that the system can be suitably used for equipment that should not be stopped.

As described above and described with reference to a preferred embodiment for illustrating the technical idea of the present invention, the present invention is not limited to the configuration and operation as shown and described as described above, it is a deviation from the scope of the technical idea It will be understood by those skilled in the art that many modifications and variations can be made to the invention without departing from the scope of the invention. Accordingly, all such suitable changes and modifications and equivalents should be considered to be within the scope of the present invention.

Claims (7)

A speed commander 110 outputting a command speed separately designated by a user, a speed controller 120 outputting a current command value to follow a command speed output from the speed commander, and a current command value output from the speed controller; Current controller 130 for outputting a voltage to follow the power, power converter 140 for converting the voltage output from the current controller to a three-phase AC power supply to the embedded permanent magnet synchronous motor, and the current flowing in the IPMSM After estimating the speed and position of the motor rotor using the current detector 150 and the current detected by the current detector, the estimated speed is SVPWM and applied to the IPMSM through the inverter, and the estimated position is the current controller. An adaptive integral binary observer consisting of an adaptive integral binary observer 170 which is an estimator provided by In the group control device 100, The motor control device 100, Purge means 161 for converting the fuzzy logic controller into a new fuzzy value (information value) that can be handled by the fuzzy logic controller according to the speed deviation and the variation of the speed deviation, and the result of the fuzzy control on the fuzzy input. Rule-based setting means 162 for deriving the derivation means, and non-fuzzy means 163 for performing a decoding operation for expressing the information output through the rule-based setting means in a specific numerical value. A fuzzy logic controller 160 which compares the errors and calculates the errors occurring therebetween, and then performs a function of determining the rate of change of the errors and the actual speed; An electric motor controller using an adaptive integral binary observer and fuzzy logic controller, comprising: a. The method of claim 1, The rule-based setting means 162, An electric motor controller using an adaptive integral binary observer and a fuzzy logic controller, characterized in that the operation is quantified through a minimum operator or a scalar product operation to derive a fuzzy control result. The method of claim 1, The non-fuzzy means 163, An electric motor controller using an adaptive integral binary observer and fuzzy logic controller, characterized by obtaining a definite value for the degree of output (purge value) by using a center of gravity (COG). A motor control method using an adaptive integral binary observer and a fuzzy logic controller, A first step of calculating an estimated speed when a load change or a change in command speed occurs; A second process of outputting a first command current through the power converter 140; A third step of calculating an error current between the first command current and the actual current; A fourth process of calculating a second command current according to the error current through a PI controller; A fifth process of acquiring a final command current; A sixth step of calculating a command voltage using the final command current by a rotor voltage method of IPMSM; And A seventh step of determining a continuous switching function having an integral switching plane; Including, The second process, Obtaining a speed error by comparing the command speed with the estimated speed; Obtaining a rate of change of the speed error by comparing the current speed error with the previous step speed error; Calculating a FLC current using the rate of change of the speed error; And Acquiring a first command current from the speed error change rate through a fixed PI controller; An electric motor control method using an adaptive integral binary observer and fuzzy logic controller, comprising: a. The method according to claim 4, Before the first process, Outputting a command speed separately designated by the user; Measuring a real current applied to the IPMSM; And Calculating an estimated speed from the state equation of the IPMSM; An electric motor control method using an adaptive integral binary observer and fuzzy logic controller, further comprising a. The method of claim 1, After the second process, Passing through a PI controller to calculate an error between the first command current and the actual current; An electric motor control method using an adaptive integral binary observer and fuzzy logic controller, further comprising a. The method of claim 4, wherein The process of obtaining the final command current, An electric motor control method using an adaptive integral binary observer and a fuzzy logic controller, characterized in that a final estimated current is obtained by adding a result calculated from a fuzzy logic controller to a value obtained by a fixed PI gain.

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