WO2022252289A1 - Mtpa control method using d-q axis inductance parameter identification of fuzzy-logical controlled permanent-magnet synchronous electric motor - Google Patents

Mtpa control method using d-q axis inductance parameter identification of fuzzy-logical controlled permanent-magnet synchronous electric motor Download PDF

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WO2022252289A1
WO2022252289A1 PCT/CN2021/100296 CN2021100296W WO2022252289A1 WO 2022252289 A1 WO2022252289 A1 WO 2022252289A1 CN 2021100296 W CN2021100296 W CN 2021100296W WO 2022252289 A1 WO2022252289 A1 WO 2022252289A1
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current
model
control
axis
axis inductance
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French (fr)
Chinese (zh)
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刘国海
安兴科
陈前
赵文祥
宋向金
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江苏大学
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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

Definitions

  • the invention relates to the technical field of model predictive control of permanent magnet synchronous motors, in particular to an improved model reference adaptive parameter identification using fuzzy logic control, which is applied in the model prediction MTPA of permanent magnet synchronous motors (Maximum torque current ratio, Maximum torque per sample) control method.
  • the method can improve the operating efficiency of the motor and the robustness of the model predictive control. It is suitable for high-efficiency permanent magnet electric drive system, new energy electric vehicle, ship propulsion system and other fields.
  • the manufacturer will provide some motor parameter nameplates or manuals after the motor is manufactured, but parameters such as d-q axis inductance and flux linkage need to be determined by the user. Even if the same motor body design drawing is provided to different manufacturers, the motor parameters will be different due to the manufacturing process and level of different manufacturers. Although the motor parameters can be measured offline, there will be a certain deviation between the actual parameters of the online motor running and the offline identification results. Therefore, it is of practical value to study the online parameter identification of permanent magnet motors.
  • the present invention utilizes the advantages and characteristics of fuzzy-logical control (Fuzzy-logical control), such as strong robustness and independent model control, to replace the traditional PI adaptive law in model reference self-adaptation with Fuzzy-PI.
  • fuzzy-logical control Fuzzy-logical control
  • the identified parameters are updated in real time in the model predictive control and formula method to realize MTPA control. Therefore, the present invention proposes an MTPA control method for d-q axis inductance parameter identification of a permanent magnet synchronous motor using fuzzy logic control.
  • the present invention adopts following technical scheme:
  • An MTPA control method for d-q axis inductance parameter identification of a permanent magnet synchronous motor using fuzzy logic control comprising the following steps:
  • Step 1 first deduce the MTPA current angle calculation formula of the formula method, when the motor parameters are updated in real time, the MTPA control of the permanent magnet motor can be realized, and the discrete model predictive control equation of the permanent magnet synchronous motor can be derived to realize the motor predictive current control;
  • Step 2 According to the principle of Popov’s ultra-stability theory, and applied in the model reference adaptive parameter identification method, then, the d-q axis inductance identification adaptive rate expression of the permanent magnet synchronous motor is deduced.
  • the identified d-q axis inductance Continuously feed back to the adjustable model until the error with the reference model is almost zero, then the identified d-q axis inductance parameters are obtained;
  • Step 3 Apply fuzzy logic control to model reference adaptation, derive the proportional and integral gain adjustment factors of the adaptive law, and adjust the d-q axis inductance together with the proportional and integral gain in model reference adaptation, then an improved model can be obtained Refer to the d-q axis inductance parameters of adaptive identification;
  • Step 4 use the d-q axis inductance parameters of the improved model reference adaptive identification to the MTPA current angle calculation formula and predictive current control to replace the original fixed parameters and update in real time, so as to realize the MTPA control and elimination of the permanent magnet motor
  • the impact of inductance mismatch in current predictive control can improve the operating efficiency of permanent magnet synchronous motors and increase the robustness of control system parameters.
  • Step 1-1 compare the given speed n * of the five-phase permanent magnet motor with the feedback speed n to obtain the speed error e r of the motor, and pass the speed error through the PI controller to obtain the speed loop of the five-phase permanent magnet motor error signal I s ;
  • Step 1-2 the reference current i d * and i q * under the dq axis of the rotating coordinate system are calculated by the following MTPA current angle formula ⁇ MTPA ;
  • L ⁇ d (k+1), L ⁇ q (k+1) are the dq axis inductance parameters identified online respectively, which are given by the online identification of the proposed model reference adaptive (PMRAS) parameters; ⁇ f is the permanent Magnetic flux linkage parameters; I s is the output of the speed loop PI controller.
  • PMRAS model reference adaptive
  • Step 1-3 the value function makes a difference between the current i p (k+2) output by the current prediction and the reference current, and according to the minimum difference of the current after the difference, selects the corresponding vector size and acts on the PWM to generate the trigger for driving the inverter Signal;
  • Steps 1-4 generate S abcde bridge arm switching sequence by PWM to drive the combination of switching trigger sequence and pulse width of the inverter to generate phase current I abcde and rotor position angle ⁇ e ;
  • Step 1-5 the phase current is changed through Park, and the current I abcde in the natural coordinate system is converted into the current i dq in the rotating coordinate system;
  • Steps 1-6, i dq is transformed into the input signal i p (k+1) controlled by the predictive model through Euler discrete transformation;
  • the input signals for online identification of PMRAS parameters are the rotating coordinate system dq axis voltage current u dq , i dq and rotor angular velocity ⁇ m , and the identified L ⁇ d (k+1) and L ⁇ q (k+1 ) update L d and L q in model predictive control and MTPA;
  • i d , i q are the dq axis currents
  • u d , u q are the dq axis voltages
  • ix , i y are the xy axis currents of the third space
  • u x , u y are the xy axis voltages of the third space
  • R s is the stator Resistance
  • ⁇ is the rotor angular velocity in the motor
  • ⁇ f is the permanent magnet flux linkage
  • T is the sampling period
  • k is the sampling sequence
  • L ⁇ d (k+1), L ⁇ q (k+1) Respectively, the dq axis inductance parameters are identified online, which are given by the proposed model reference adaptive (PMRAS) parameter online identification;
  • PMRAS proposed model reference adaptive
  • the voltage signal V abcde can obtain the current signal I abcde through the reference model, where i d , i q are the dq axis currents, u d , u q are the dq axis voltages, R s is the stator resistance, L d , L q is the inductance of the dq axis, ⁇ m is the angular velocity of the rotor in the motor, ⁇ f is the flux linkage of the rotor, the current in the five-phase natural coordinate system is converted into the current i dq in the rotating coordinate system through Park transformation;
  • Step 2-2 the voltage signal V abcde converts the voltage in the five-phase natural coordinate system into the voltage u dq in the rotating coordinate system through Park transformation, and uses it as the input signal of the adjustable model;
  • ⁇ indicates the parameter or signal that needs to be identified
  • step 2-3 the current signal i d and i q of the reference model are compared with the current signal i d ⁇ and i q ⁇ of the adjustable model to obtain the output signal of the current error change e(t), which contains the need to identify Parameter information;
  • model reference adaptation rate is derived as follows:
  • step 3-1 in order to describe the equations concisely, the reference model formula in step 2-1 and the adjustable model formula in step 2-2 are respectively rewritten in the following standard form:
  • the above symbol ".” indicates a differential operator.
  • the adjustable model is established according to the standard form. Except for the inductance and current of the dq axis, all parameters are completely consistent with the reference model.
  • the identification value is represented by the symbol " ⁇ "; where u dq is the stator axis Voltage, i dq is the stator shaft current, R s is the stator phase resistance, ⁇ e is the electrical angular velocity, ⁇ f is the flux linkage of the permanent magnet of the motor;
  • Step 3-2 rewrite the adjustable model as follows:
  • Step 3-3 according to the principle of Popov's ultra-stability theory, if the feedback system is to remain stable, the nonlinear loop should satisfy the following formula
  • ⁇ (0,t) is an integral function
  • r 2 is a finite normal quantity independent of the integral upper limit t
  • W is a nonlinear feedback input
  • w1 is an intermediate variable
  • w1 -W.
  • the design purpose of the parameter adaptive law is to estimate the required parameters online, and then make the generalized error of the control system gradually tend to zero through feedback adjustment; the design of the general adaptive law usually adopts the adjustment principle of proportional integral; according to the superstable law In order to meet the super stability of the model reference adaptive control system, not only must the linear steady forward channel be strictly positive, but also the nonlinear feedback loop must satisfy the integral inequality, so a series of derivation processes are omitted, and the adaptive law expression of the motor parameters can be obtained formula.
  • L ⁇ d is the direct-axis inductance parameter to be identified
  • L d is the reference direct-axis inductance parameter
  • R 1 ( ⁇ ) is the integral function of ⁇
  • R 2 ( ⁇ ) is the function of ⁇ .
  • the symbol ".” represents the differential operator
  • ⁇ is the current output difference between the reference model and the adjustable model
  • ⁇ 1 is the direct-axis current output difference between the reference model and the adjustable model.
  • f(t) is an integrable function about t
  • K p1 and K i1 are the proportional integral gains of the direct-axis inductance parameter L ⁇ d adaptation rate
  • K p2 and K i2 are the proportional integral gains of the quadrature-axis inductance parameter L ⁇ q adaptation rate respectively.
  • fuzzy logic control is applied in model reference self-adaptation, and the specific process is derived as follows:
  • Step 4-1 the current signal i d and i q of the reference model, and the current signal i d ⁇ and i q ⁇ of the adjustable model are different to obtain the output signal of the current error change e(t), and it contains the need to identify Parameter information;
  • Step 4-2 the proportional gain Kc and the differential gain Ke are respectively multiplied by the current error to obtain the input current error E and its rate of change Ec of the fuzzy logic control, and then input the above to the fuzzy logic control;
  • Step 4-3 defuzzification, convert the implicit fuzzy control set into explicit output through the center of gravity defuzzification technology, fuzzy reasoning adopts the direct product method,
  • u ei (e), u ei (de/dt) are the current error and the level of changing the current error respectively;
  • Step 4-4 thus deriving the i-th control rule from the above product operation
  • the degree of membership is u
  • the output of the controller is ⁇ L n
  • Rule is the fuzzy inference rule shown in Figure 2(e)
  • Rule i ( ⁇ L s ) represents the i-th control rule of the controller output ⁇ L s Control decision-making membership degree
  • u ⁇ L′ n ( ⁇ L s ) is the membership function of the controller output ⁇ L s in the fuzzy set ⁇ L′ n in the domain of discourse
  • ⁇ L s is the membership function as shown in Figure 2(a)-(d) ;
  • Step 4-5 the membership function u ⁇ L n of the controller output ⁇ L n is;
  • Steps 4-6 finally use the center of gravity method to defuzzify, and the precise value of the output can be obtained in the discrete domain;
  • ⁇ Z s is the specific output value of fuzzy control in the discrete domain
  • u ⁇ L n ⁇ L si is the membership degree coefficient in the i-th control rule in the discrete domain
  • C( ⁇ L si ) is the corresponding membership in the i-th control rule degree function
  • Steps 4-7 multiply the output error by the current error to obtain ⁇ K p and ⁇ K i , and continuously adjust the output through K u ,
  • ⁇ K p and ⁇ K i are the proportional and integral gain adjustment factors of fuzzy output respectively, and K u is the adjustment output scaling gain;
  • ⁇ K p and ⁇ K i are used to adjust the dq-axis inductance together with the proportional and integral gains in the model reference adaptation, and then the dq-axis inductance parameters for improved model reference adaptive identification can be obtained.
  • the MTPA control under the model predictive control can be realized.
  • the present invention makes full use of the good robustness of fuzzy logic control, the system does not change from time to time, and does not depend on precise control models. Therefore, replacing the traditional PI controller in the adaptive law with Fuzzy-PI can improve the interference of MRAS identification parameters on sudden changes in speed and load torque, and improve the accuracy of parameter identification.
  • the d-q axis parameters identified by the present invention replace the constant parameters in the model predictive control to obtain better control performance, enhance the robustness of the model predictive control and improve the control performance of the motor.
  • the present invention uses the parameters identified by the Fuzzy-PI adjustment in the adaptive law in the formula method MTPA control, which can automatically find the best MTPA operating point, and obtain the minimum phase current amplitude under the same torque output, thereby Reduce the copper consumption of the motor and improve the operating efficiency of the motor.
  • Figure 1 (a) MTPA control block diagram for model prediction of five-phase permanent magnet synchronous motor; (b) MRAS parameter identification under Fuzzy-PI control.
  • Figure 2 Fuzzy logic member functions and logic rules.
  • (a) Membership function of d-axis error and error change (b) Membership function of ⁇ k p1 and ⁇ k i1 output (c) Membership function of q-axis error and error change (d) Membership function of ⁇ k p2 and ⁇ k i2 output (e) Fuzzy control rules.
  • Figure 3 The identified dq-axis inductance of the MRAS under a speed step. (a) L d (b) L q .
  • Figure 4 The identified dq-axis inductance of the MRAS under a torque step. (a) L d (b) L q .
  • Figure 6 Comparison of model-predicted current and torque outputs with and without parameter identification in the case of parameter mismatch.
  • (a) Overall comparison chart of model prediction with and without parameter identification (b) THD content without parameter identification (c) THD content with parameter identification.
  • the invention discloses a model predictive MTPA control method for parameter identification of permanent magnet synchronous motors controlled by fuzzy logic. Obtain the given current I s of the motor; then use the Fuzzy-PI adjustment model to refer to L dq (k+1) identified by self-adaptation to update MTPA in real time to obtain the reference current i dq *, and the model predicts the constant parameter L dq by L dq (k +1) Instead, the value function makes the difference between the current i p (k+2) output by the current prediction and the reference voltage, and selects the size of the corresponding vector according to the minimum difference of the current as the order of PWM generation; generated by the inverter
  • the phase current I abcde and the position angle ⁇ e of the embedded permanent magnet motor the phase current is changed through Park, and the phase current in the natural coordinate system is converted into the current i dq in the rotating coordinate system; i dq is transformed through Euler discretization
  • FIG. 1(a) An MTPA control method for d-q axis inductance parameter identification of permanent magnet synchronous motor using fuzzy logic control is shown in Fig. 1(a).
  • Step 1-1 comparing the given speed n * of the five-phase permanent magnet motor with the feedback speed n, the speed error e r of the motor can be obtained, and the speed error of the five-phase permanent magnet motor can be obtained through the PI controller ring error signal I s ;
  • Step 1-2 the reference current i d * and i q * under the dq axis of the rotating coordinate system are obtained by the following MTPA formula
  • L ⁇ dq (k+1) is the dq-axis inductance identified online, which is given by the online identification of the parameters of the proposed model reference adaptive system (PMRAS);
  • Step 1-3 the value function makes a difference between the current i p (k+2) output by the current prediction and the reference current, and according to the minimum difference of the current after the difference, selects the corresponding vector size and acts on the PWM to generate the trigger for driving the inverter Signal;
  • Steps 1-4 generate S abcde bridge arm switching sequence by PWM to drive the combination of switching trigger sequence and pulse width of the inverter to generate phase current I abcde and rotor position angle ⁇ e ;
  • Steps 1-5 the phase current is changed through Park, and the current in the natural coordinate system is converted into the current idq in the rotating coordinate system;
  • Steps 1-6 convert i dq into input signal i p (k+1) controlled by predictive model through Euler discretization
  • the input signals for online identification of PMRAS parameters are the rotating coordinate system dq axis voltage current u dq , i dq and rotor angular velocity ⁇ m , and the identified L ⁇ d (k+1) and L ⁇ q (k+1 ) update L d and L q in model predictive control and MTPA;
  • i d , i q are dq axis currents
  • u d dq axis voltages
  • R s stator resistance
  • L ls leakage inductance ⁇ is rotor angular velocity in the motor
  • T is the sampling period
  • k is the sampling sequence
  • Steps 1-10 after the above steps, the MTPA control under the model predictive control can be realized.
  • the voltage signal V abcde can obtain the current signal I abcde through the reference model, where i d , i q are the dq axis currents, u d , u q are the dq axis voltages, R s is the stator resistance, L d , L q is the inductance of the dq axis, ⁇ e is the angular velocity of the rotor in the motor, ⁇ f is the flux linkage of the rotor, and the current in the five-phase natural coordinate system is transformed into the current i dq in the rotating coordinate system through Park transformation;
  • Step 2-2 the voltage signal V abcde converts the voltage in the five-phase natural coordinate system into the voltage u dq in the rotating coordinate system through Park transformation, and uses it as the input signal of the adjustable model;
  • ⁇ indicates the parameter or signal that needs to be identified
  • step 2-3 the current signal i d and i q of the reference model are compared with the current signal i d ⁇ and i q ⁇ of the adjustable model to obtain the output signal of the current error change e(t), which contains the need to identify Parameter information;
  • Step 2-4 the proportional gain Kc and the differential gain Ke are multiplied by the current error respectively to obtain the input current error (E) and its change rate (Ec) of the fuzzy logic control, and then input the above to the fuzzy logic control, the specific process is detailed see part three;
  • the parameter identification method proposed by the present invention is closer to the reference value and has better identification accuracy; Fig. 3 and Fig. 4 are in 0.1 second
  • the traditional method is disturbed by medium torque and speed step, but the method proposed by the present invention has good robustness, which proves the correctness of the proposed method.
  • references to the terms “one embodiment,” “some embodiments,” “exemplary embodiments,” “example,” “specific examples,” or “some examples” are intended to mean that the implementation A specific feature, structure, material, or characteristic described by an embodiment or example is included in at least one embodiment or example of the present invention.
  • schematic representations of the above terms do not necessarily refer to the same embodiment or example.
  • the specific features, structures, materials or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

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  • Automation & Control Theory (AREA)
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Abstract

Disclosed in the present application is an MTPA control method using d-q axis inductance parameter identification of a fuzzy-logical controlled permanent-magnet synchronous electric motor. By means of the method, the advantages of fuzzy-logical control, such as strong robustness and not depending on model control, are fully utilized, and the traditional PI in a model reference adaptive law is replaced with Fuzzy-PI, thereby improving the accuracy and robustness of traditional model reference adaptive identification. Then, an identified d-q axis inductance is updated into an MTPA formula in real time, such that the current amplitude under the same torque output is minimized, thereby reducing the copper consumption of an electric motor, and improving the operating efficiency; and the parameter is also used in model predictive control to replace an original fixed parameter, so as to eliminate the disturbance caused by electric motor parameter mismatch to a control system, such that the robustness of the control system is enhanced, thereby obtaining a better control performance.

Description

采用模糊逻辑控制的永磁同步电机d-q轴电感参数辨识的MTPA控制方法MTPA control method for d-q axis inductance parameter identification of permanent magnet synchronous motor using fuzzy logic control 技术领域technical field
本发明涉及永磁同步电机模型预测控制技术领域，特别涉及一种采用模糊逻辑控制的改进模型参考自适应参数辨识，应用在永磁同步电机模型预测MTPA中(最大转矩电流比，Maximum torque per ample)的控制方法。该方法可以提高电机的运行效率，以及提高模型预测控制的鲁棒性。其适用于高效率永磁电驱动装置系统、新能源电动汽车、舰船推进系统等领域。The invention relates to the technical field of model predictive control of permanent magnet synchronous motors, in particular to an improved model reference adaptive parameter identification using fuzzy logic control, which is applied in the model prediction MTPA of permanent magnet synchronous motors (Maximum torque current ratio, Maximum torque per sample) control method. The method can improve the operating efficiency of the motor and the robustness of the model predictive control. It is suitable for high-efficiency permanent magnet electric drive system, new energy electric vehicle, ship propulsion system and other fields.
背景技术Background technique
永磁电机因为其高转矩密度、高效率以及高可靠性等特点，在新能源电动汽车牵引、航天航空以及工业生产领域应用越来越广泛。同时，由于电机运行温度上升、磁饱和等因素，以及电机运行受转速、转矩阶跃扰动影响，永磁电机的参数会发生变化。然而，永磁同步电机采用模型预测控制和公式法实现MTPA控制都严重依赖电机参数。因此，实时更新实际电机参数对于实现上述控制方法具有重要的意义。Due to its high torque density, high efficiency and high reliability, permanent magnet motors are more and more widely used in the fields of new energy electric vehicle traction, aerospace and industrial production. At the same time, due to factors such as motor operating temperature rise, magnetic saturation, and motor operation being affected by speed and torque step disturbances, the parameters of the permanent magnet motor will change. However, both the model predictive control and the formula method to realize the MTPA control of the permanent magnet synchronous motor are heavily dependent on the motor parameters. Therefore, updating the actual motor parameters in real time is of great significance for realizing the above control method.
对于永磁同步电机来说，厂商在加工制造电机后会提供一些电机参数铭牌或手册，但是诸如d-q轴电感，磁链等参数都需要使用者测定。即使把相同的电机本体设计图提供给不同加工厂商，也由于不同加工厂商的制作工艺及其水平都会造成电机参数的不同。虽然离线可以测出电机参数，但是在线电机运行时的实际参数与离线辨识结果会产生一定的偏差。因此研究永磁电机的在线参数辨识具有实际的价值。For permanent magnet synchronous motors, the manufacturer will provide some motor parameter nameplates or manuals after the motor is manufactured, but parameters such as d-q axis inductance and flux linkage need to be determined by the user. Even if the same motor body design drawing is provided to different manufacturers, the motor parameters will be different due to the manufacturing process and level of different manufacturers. Although the motor parameters can be measured offline, there will be a certain deviation between the actual parameters of the online motor running and the offline identification results. Therefore, it is of practical value to study the online parameter identification of permanent magnet motors.
近年来，传统的在线辨识算法有信号注入法、最小二乘法、卡尔曼滤波算法、模型参考自适应等，高级的算法有人工智能、神经网络、蚁群算法等都被应用在参数辨识方面，然而上述算法都具有各自的优缺点。由于模糊逻辑控制是一种先进的控制方法，它对时变系统具有良好的鲁棒性，不依赖于精确模型等特点。通过把传统模型预测控制和高级控制算法相结合，能够实现较好的辨识结果。而把辨识到的参数实时更新在依赖参数的模型预测控制中，会实现较为理想的控制效果。In recent years, traditional online identification algorithms include signal injection method, least square method, Kalman filter algorithm, model reference adaptive, etc. Advanced algorithms such as artificial intelligence, neural network, ant colony algorithm, etc. have been applied in parameter identification. However, the above algorithms all have their own advantages and disadvantages. Since fuzzy logic control is an advanced control method, it has good robustness to time-varying systems and does not depend on precise models. Better identification results can be achieved by combining traditional model predictive control with advanced control algorithms. And updating the identified parameters in real time in the parameter-dependent model predictive control will achieve a more ideal control effect.
发明内容Contents of the invention
本发明利用模糊逻辑控制(Fuzzy-logical control)的鲁棒性强、不依赖模型控制等优点特点，将模型参考自适应中的传统PI自适应律用Fuzzy-PI替代。同时将辨识到的参数实时 更新在模型预测控制和公式法实现MTPA控制中。因此，本发明提出一种采用模糊逻辑控制的永磁同步电机d-q轴电感参数辨识的MTPA控制方法。The present invention utilizes the advantages and characteristics of fuzzy-logical control (Fuzzy-logical control), such as strong robustness and independent model control, to replace the traditional PI adaptive law in model reference self-adaptation with Fuzzy-PI. At the same time, the identified parameters are updated in real time in the model predictive control and formula method to realize MTPA control. Therefore, the present invention proposes an MTPA control method for d-q axis inductance parameter identification of a permanent magnet synchronous motor using fuzzy logic control.
为达到技术目的，本发明采用如下技术方案：For achieving technical purpose, the present invention adopts following technical scheme:
一种采用模糊逻辑控制的永磁同步电机d-q轴电感参数辨识的MTPA控制方法，包括如下步骤：An MTPA control method for d-q axis inductance parameter identification of a permanent magnet synchronous motor using fuzzy logic control, comprising the following steps:
步骤1，首先推导出公式法的MTPA电流角计算公式，当电机参数实时更新时候便可以实现永磁电机的MTPA控制，以及推导出永磁同步电机离散化的模型预测控制方程可实现对该电机的预测电流控制； Step 1, first deduce the MTPA current angle calculation formula of the formula method, when the motor parameters are updated in real time, the MTPA control of the permanent magnet motor can be realized, and the discrete model predictive control equation of the permanent magnet synchronous motor can be derived to realize the motor predictive current control;
步骤2，根据Popov超稳定性理论原理，并应用在模型参考自适应参数辨识方法中，然后，推导出了永磁同步电机的d-q轴电感辨识自适应率表达式，当辨识到的d-q轴电感不断反馈到可调模型直到与参考模型误差几乎为零，则得到辨识的d-q轴电感参数； Step 2. According to the principle of Popov’s ultra-stability theory, and applied in the model reference adaptive parameter identification method, then, the d-q axis inductance identification adaptive rate expression of the permanent magnet synchronous motor is deduced. When the identified d-q axis inductance Continuously feed back to the adjustable model until the error with the reference model is almost zero, then the identified d-q axis inductance parameters are obtained;
步骤3，将模糊逻辑控制应用在模型参考自适应中，推导出自适应律的比例、积分增益调整因子，并与模型参考自适应中的比例、积分增益共同调节d-q轴电感，则可得改进模型参考自适应辨识的d-q轴电感参数；Step 3: Apply fuzzy logic control to model reference adaptation, derive the proportional and integral gain adjustment factors of the adaptive law, and adjust the d-q axis inductance together with the proportional and integral gain in model reference adaptation, then an improved model can be obtained Refer to the d-q axis inductance parameters of adaptive identification;
步骤4，将改进模型参考自适应辨识的d-q轴电感参数使用到MTPA电流角计算公式和预测电流控制中，以替代原有的固定参数和实时更新，即可实现永磁电机的MTPA控制和消除电流预测控制中电感失配带来的影响，从而能提高永磁同步电机的运行效率和增大控制系统参数鲁棒性。 Step 4, use the d-q axis inductance parameters of the improved model reference adaptive identification to the MTPA current angle calculation formula and predictive current control to replace the original fixed parameters and update in real time, so as to realize the MTPA control and elimination of the permanent magnet motor The impact of inductance mismatch in current predictive control can improve the operating efficiency of permanent magnet synchronous motors and increase the robustness of control system parameters.
首先按照所述步骤1，具体过程依次如下：First follow step 1, the specific process is as follows:
步骤1-1，五相永磁电机的给定转速n *，与反馈转速n，相比较得到电机的转速误差e r，将该转速误差通过PI控制器可得到五相永磁电机的速度环误差信号I s； Step 1-1, compare the given speed n * of the five-phase permanent magnet motor with the feedback speed n to obtain the speed error e r of the motor, and pass the speed error through the PI controller to obtain the speed loop of the five-phase permanent magnet motor error signal I s ;
步骤1-2，旋转坐标系d-q轴下的参考电流i d *、i q *由以下MTPA电流角公式γ MTPA计算得到； Step 1-2, the reference current i d * and i q * under the dq axis of the rotating coordinate system are calculated by the following MTPA current angle formula γ MTPA ;
Figure PCTCN2021100296-appb-000001
Figure PCTCN2021100296-appb-000001
Figure PCTCN2021100296-appb-000002
Figure PCTCN2021100296-appb-000002
其中：L ^ d(k+1)、L ^ q(k+1)分别是在线辨识到d-q轴电感参数，是由提出的模型参考自适应(PMRAS)参数在线辨识给定；ψ f是永磁磁链参数；I s是转速环PI控制器输出。 Among them: L ^ d (k+1), L ^ q (k+1) are the dq axis inductance parameters identified online respectively, which are given by the online identification of the proposed model reference adaptive (PMRAS) parameters; ψ f is the permanent Magnetic flux linkage parameters; I s is the output of the speed loop PI controller.
步骤1-3，价值函数将电流预测输出的电流i p(k+2)和参考电流做差，并且根据做差后的电流最小差值，选取对应矢量大小作用PWM生成驱动逆变器的触发信号； Step 1-3, the value function makes a difference between the current i p (k+2) output by the current prediction and the reference current, and according to the minimum difference of the current after the difference, selects the corresponding vector size and acts on the PWM to generate the trigger for driving the inverter Signal;
步骤1-4，由PWM生成S abcde桥臂开关顺序，来驱动逆变器的开关触发顺序和脉宽大小组合生成相电流I abcde和转子位置角θ e； Steps 1-4, generate S abcde bridge arm switching sequence by PWM to drive the combination of switching trigger sequence and pulse width of the inverter to generate phase current I abcde and rotor position angle θ e ;
步骤1-5，相电流经过Park变化，将自然坐标系下的电流I abcde转换成旋转坐标系下的电流i dq； Step 1-5, the phase current is changed through Park, and the current I abcde in the natural coordinate system is converted into the current i dq in the rotating coordinate system;
步骤1-6，i dq经过欧拉离散转换成预测模型控制的输入信号i p(k+1)； Steps 1-6, i dq is transformed into the input signal i p (k+1) controlled by the predictive model through Euler discrete transformation;
步骤1-7，PMRAS参数在线辨识的输入信号分别为旋转坐标系d-q轴电压电流u dq，i dq和转子角速度ω m，辨识的L ^ d(k+1)和L ^ q(k+1)更新模型预测控制和MTPA中的L d和L q； Steps 1-7, the input signals for online identification of PMRAS parameters are the rotating coordinate system dq axis voltage current u dq , i dq and rotor angular velocity ω m , and the identified L ^ d (k+1) and L ^ q (k+1 ) update L d and L q in model predictive control and MTPA;
步骤1-8，电流预测通过辨识出的d-q轴电感L ^ d(k+1)和L ^ q(k+1)，选择矢量V i(i＝1,…12),离散的输入电流i p(k+1)而生成模型预测控制电流i p(k+2)； Steps 1-8, current prediction through the identified dq-axis inductance L ^ d (k+1) and L ^ q (k+1), select vector V i (i=1,...12), discrete input current i p (k+1) while generating model predictive control current i p (k+2);
Figure PCTCN2021100296-appb-000003
Figure PCTCN2021100296-appb-000003
其中，i d、i q是d-q轴电流，u d、u q是d-q轴电压，i x、i y是三次空间x-y轴电流，u x、u y是三次空间x-y轴电压，R s是定子电阻，L ls漏感，ω是电机中的转子角速度，ψ f是永磁磁链，T为采样周期，k为采样顺序；L ^ d(k+1)、L ^ q(k+1)分别是在线辨识到d-q轴电感参数，是由提出的模型参考自适应(PMRAS)参数在线辨识给定； Among them, i d , i q are the dq axis currents, u d , u q are the dq axis voltages, ix , i y are the xy axis currents of the third space, u x , u y are the xy axis voltages of the third space, R s is the stator Resistance, L ls leakage inductance, ω is the rotor angular velocity in the motor, ψ f is the permanent magnet flux linkage, T is the sampling period, k is the sampling sequence; L ^ d (k+1), L ^ q (k+1) Respectively, the dq axis inductance parameters are identified online, which are given by the proposed model reference adaptive (PMRAS) parameter online identification;
然后再依照所述步骤2，具体过程分别如下：Then follow the step 2, the specific process is as follows:
步骤2-1，电压信号V abcde通过参考模型可得到电流信号I abcde，其中，i d、i q是d-q轴电流，u d、u q是d-q轴电压，R s是定子电阻，L d、L q是d-q轴电感，ω m是电机中的转子角速度，ψ f是转子磁链，该电流经过Park变换将五相自然坐标系下的电流转换成旋转坐标系下的电流i dq； Step 2-1, the voltage signal V abcde can obtain the current signal I abcde through the reference model, where i d , i q are the dq axis currents, u d , u q are the dq axis voltages, R s is the stator resistance, L d , L q is the inductance of the dq axis, ω m is the angular velocity of the rotor in the motor, ψ f is the flux linkage of the rotor, the current in the five-phase natural coordinate system is converted into the current i dq in the rotating coordinate system through Park transformation;
模型参考自适应的参考模型方程表示为：The reference model equation for model reference adaptation is expressed as:
Figure PCTCN2021100296-appb-000004
Figure PCTCN2021100296-appb-000004
步骤2-2，电压信号V abcde通过Park变换将五相自然坐标系下的电压转换成旋转坐标系下的电压u dq，并作为可调模型的输入信号； Step 2-2, the voltage signal V abcde converts the voltage in the five-phase natural coordinate system into the voltage u dq in the rotating coordinate system through Park transformation, and uses it as the input signal of the adjustable model;
模型参考自适应的可调模型方程表示为：The adjustable model equation for model reference adaptation is expressed as:
Figure PCTCN2021100296-appb-000005
Figure PCTCN2021100296-appb-000005
其中，符号“^”表示需要辨识的参数或者信号；Among them, the symbol "^" indicates the parameter or signal that needs to be identified;
步骤2-3，经过参考模型的电流信号i d和i q，与可调模型电流信号i d ^和i q ^做差可得到电流误差变化e(t)的输出信号，且含有需要辨识的参数信息； In step 2-3, the current signal i d and i q of the reference model are compared with the current signal i d ^ and i q ^ of the adjustable model to obtain the output signal of the current error change e(t), which contains the need to identify Parameter information;
Figure PCTCN2021100296-appb-000006
Figure PCTCN2021100296-appb-000006
进一步，模型参考自适应率推导如下：Further, the model reference adaptation rate is derived as follows:
步骤3-1，为了简洁地描述方程，分别将步骤2-1的参考模型公式和步骤2-2的可调模型公式，用以下标准形式重写为：In step 3-1, in order to describe the equations concisely, the reference model formula in step 2-1 and the adjustable model formula in step 2-2 are respectively rewritten in the following standard form:
i&＝Ai+Bu+Cwi&＝Ai+Bu+Cw
其中，in,
Figure PCTCN2021100296-appb-000007
Figure PCTCN2021100296-appb-000007
上述符号“.”表示差分运算符，根据标准形式建立可调模型，除d-q轴电感和电流外，所有参数与参考模型完全一致，辨识值用符号“^”表示；式中u dq为定子轴电压,i dq为定子轴电流，R s为定子相电阻,ω e是电角速度，ψ f是电机永磁体磁链； The above symbol "." indicates a differential operator. The adjustable model is established according to the standard form. Except for the inductance and current of the dq axis, all parameters are completely consistent with the reference model. The identification value is represented by the symbol "^"; where u dq is the stator axis Voltage, i dq is the stator shaft current, R s is the stator phase resistance, ω e is the electrical angular velocity, ψ f is the flux linkage of the permanent magnet of the motor;
步骤3-2，把可调模型改写如下：Step 3-2, rewrite the adjustable model as follows:
Figure PCTCN2021100296-appb-000008
Figure PCTCN2021100296-appb-000008
其中,
Figure PCTCN2021100296-appb-000009
in,
Figure PCTCN2021100296-appb-000009
步骤3-3，根据Popov超稳定性理论的原理，如果反馈系统要保持稳定，则非线性回路应满足以下公式Step 3-3, according to the principle of Popov's ultra-stability theory, if the feedback system is to remain stable, the nonlinear loop should satisfy the following formula
Figure PCTCN2021100296-appb-000010
Figure PCTCN2021100296-appb-000010
且有and have
Figure PCTCN2021100296-appb-000011
Figure PCTCN2021100296-appb-000011
其中，η(0,t)为积分函数，r 2为一个不依赖积分上限t的有限正常量，W是非线性反馈输入，w1为中间变量且w1＝-W。 Among them, η(0,t) is an integral function, r 2 is a finite normal quantity independent of the integral upper limit t, W is a nonlinear feedback input, w1 is an intermediate variable and w1=-W.
参数自适应律的设计目的是为了在线估计出需要的参数，然后通过反馈调节使得控制系统的广义误差逐渐趋向于零；一般自适应律的设计通常采用比例积分的调节原理；根据超稳态定律为了满足模型参考自适应控制系统超稳定，不仅要满足线性定常前向通道严格正实，而且非线性反馈回路满足积分不等式，因此省略了一系列的推导过程，可以得到电机参数的自适应律表达式子。The design purpose of the parameter adaptive law is to estimate the required parameters online, and then make the generalized error of the control system gradually tend to zero through feedback adjustment; the design of the general adaptive law usually adopts the adjustment principle of proportional integral; according to the superstable law In order to meet the super stability of the model reference adaptive control system, not only must the linear steady forward channel be strictly positive, but also the nonlinear feedback loop must satisfy the integral inequality, so a series of derivation processes are omitted, and the adaptive law expression of the motor parameters can be obtained formula.
首先，分析直轴电感辨识的自适应规律，并给出了相应的计算公式，交轴自适应律的推导过程与其相似，在此略去。First, the adaptive law of direct-axis inductance identification is analyzed, and the corresponding calculation formula is given. The derivation process of quadrature-axis adaptive law is similar to it, so it is omitted here.
Figure PCTCN2021100296-appb-000012
Figure PCTCN2021100296-appb-000012
其中，L ^ d为需辨识的直轴电感参数，L d为参考直轴电感参数，R 1(τ)是关于τ的积分函数，R 2(τ)是关于τ的函数。 Among them, L ^ d is the direct-axis inductance parameter to be identified, L d is the reference direct-axis inductance parameter, R 1 (τ) is the integral function of τ, and R 2 (τ) is the function of τ.
然后，将上式代入步骤3-3方程，可得Then, substituting the above formula into the equation of step 3-3, we can get
Figure PCTCN2021100296-appb-000013
Figure PCTCN2021100296-appb-000013
其中，符号“.”表示差分运算符，ε为参考模型与可调模型电流输出差，ε 1为参考模型与可调模型直轴电流输出差。 Among them, the symbol "." represents the differential operator, ε is the current output difference between the reference model and the adjustable model, and ε1 is the direct-axis current output difference between the reference model and the adjustable model.
用以下定理求解上面公式，Solve the above formula using the following theorem,
Figure PCTCN2021100296-appb-000014
Figure PCTCN2021100296-appb-000014
且正实常数k大于0，f(t)是关于t的可积函数，And the positive real constant k is greater than 0, f(t) is an integrable function about t,
设以下式子为Let the following formula be
Figure PCTCN2021100296-appb-000015
Figure PCTCN2021100296-appb-000015
则可得到R 1(τ)＝K i1ε 1u d，只要K p1大于0，则R 2(τ)＝K p1ε 1u d，则自适应律有如下式： Then R 1 (τ)=K i1 ε 1 u d can be obtained, as long as K p1 is greater than 0, then R 2 (τ)=K p1 ε 1 u d , then the adaptive law has the following formula:
Figure PCTCN2021100296-appb-000016
Figure PCTCN2021100296-appb-000016
其中，K p1、K i1分别为直轴电感参数L ^ d自适应率的比例积分增益，K p2、K i2分别为交轴电感参数L ^ q自适应率的比例积分增益。 Among them, K p1 and K i1 are the proportional integral gains of the direct-axis inductance parameter L ^ d adaptation rate, and K p2 and K i2 are the proportional integral gains of the quadrature-axis inductance parameter L ^ q adaptation rate respectively.
进一步，模糊逻辑控制应用在模型参考自适应中，具体过程推导如下：Further, fuzzy logic control is applied in model reference self-adaptation, and the specific process is derived as follows:
步骤4-1，经过参考模型的电流信号i d和i q，与可调模型电流信号i d ^和i q ^做差可得到电流误差变化e(t)的输出信号，且含有需要辨识的参数信息； Step 4-1, the current signal i d and i q of the reference model, and the current signal i d ^ and i q ^ of the adjustable model are different to obtain the output signal of the current error change e(t), and it contains the need to identify Parameter information;
Figure PCTCN2021100296-appb-000017
Figure PCTCN2021100296-appb-000017
步骤4-2，比例增益Kc和微分增益Ke分别乘以电流误差，可得到模糊逻辑控制的输入电流误差E及其变化率Ec，然后将上述输入到模糊逻辑控制；Step 4-2, the proportional gain Kc and the differential gain Ke are respectively multiplied by the current error to obtain the input current error E and its rate of change Ec of the fuzzy logic control, and then input the above to the fuzzy logic control;
步骤4-3，解模糊，通过重心解模糊技术将隐式模糊控制集转化为显式输出，模糊推理采用直积法，Step 4-3, defuzzification, convert the implicit fuzzy control set into explicit output through the center of gravity defuzzification technology, fuzzy reasoning adopts the direct product method,
Figure PCTCN2021100296-appb-000018
Figure PCTCN2021100296-appb-000018
其中u ei(e)，u ei(de/dt)分别是当前误差和变化当前误差的等级； Among them, u ei (e), u ei (de/dt) are the current error and the level of changing the current error respectively;
步骤4-4，因此从上述产品操作中导出第i条控制规则，Step 4-4, thus deriving the i-th control rule from the above product operation,
uΔL nRule i(ΔL s)＝sup[a iuΔL′ n(ΔL s)] uΔL n Rule i (ΔL s )=sup[a i uΔL′ n (ΔL s )]
其中，隶属度为u，控制器输出为ΔL n，Rule为模糊推理规则如图2(e)所示，uΔL nRule i(ΔL s)表示控制器输出ΔL s的第i条控制规则中的控制决策隶属度，uΔL′ n(ΔL s)为控制器输出ΔL s在论域中模糊集合ΔL′ n的隶属度函数，ΔL s为隶属度函数如图2(a)-(d)所示； Among them, the degree of membership is u, the output of the controller is ΔL n , Rule is the fuzzy inference rule shown in Figure 2(e), uΔL n Rule i (ΔL s ) represents the i-th control rule of the controller output ΔL s Control decision-making membership degree, uΔL′ n (ΔL s ) is the membership function of the controller output ΔL s in the fuzzy set ΔL′ n in the domain of discourse, and ΔL s is the membership function as shown in Figure 2(a)-(d) ;
步骤4-5，控制器输出ΔL n的隶属函数uΔL n为； Step 4-5, the membership function uΔL n of the controller output ΔL n is;
Figure PCTCN2021100296-appb-000019
Figure PCTCN2021100296-appb-000019
步骤4-6，最后采用重心法进行解模糊，在离散域中可以得到输出的精确值；Steps 4-6, finally use the center of gravity method to defuzzify, and the precise value of the output can be obtained in the discrete domain;
Figure PCTCN2021100296-appb-000020
Figure PCTCN2021100296-appb-000020
其中，ΔZ s为模糊控制在离散域上的具体输出值，uΔL nΔL si为离散域上第i条控制规则中的隶属度系数，C(ΔL si)为第i条控制规则中对应的隶属度函数； Among them, ΔZ s is the specific output value of fuzzy control in the discrete domain, uΔL n ΔL si is the membership degree coefficient in the i-th control rule in the discrete domain, and C(ΔL si ) is the corresponding membership in the i-th control rule degree function;
步骤4-7，将输出误差与电流误差相乘得到ΔK p和ΔK i，并通过K u持续调整输出， Steps 4-7, multiply the output error by the current error to obtain ΔK p and ΔK i , and continuously adjust the output through K u ,
Figure PCTCN2021100296-appb-000021
Figure PCTCN2021100296-appb-000021
Figure PCTCN2021100296-appb-000022
Figure PCTCN2021100296-appb-000022
其中，ΔK p和ΔK i分别模糊输出的的比例、积分增益调整因子，K u为调整输出标度增益； Among them, ΔK p and ΔK i are the proportional and integral gain adjustment factors of fuzzy output respectively, and K u is the adjustment output scaling gain;
步骤4-8，将ΔK p和ΔK i分别与模型参考自适应中的比例、积分增益共同调节d-q轴电感，则可得改进模型参考自适应辨识的d-q轴电感参数。 In steps 4-8, ΔK p and ΔK i are used to adjust the dq-axis inductance together with the proportional and integral gains in the model reference adaptation, and then the dq-axis inductance parameters for improved model reference adaptive identification can be obtained.
Figure PCTCN2021100296-appb-000023
Figure PCTCN2021100296-appb-000023
经过上述步骤便可以实现模型预测控制下的MTPA控制。After the above steps, the MTPA control under the model predictive control can be realized.
本发明具有以下有益效果：The present invention has the following beneficial effects:
1、本发明充分利用了模糊(Fuzzy)逻辑控制的良好鲁棒性、系统时不变化、不依赖于精确控制模型等优点。因此，将自适应律中的传统PI控制器被取Fuzzy-PI所替代，可以提高MRAS辨识参数对速度、负载转矩突变的干扰，以及提高参数辨识的精确度。1. The present invention makes full use of the good robustness of fuzzy logic control, the system does not change from time to time, and does not depend on precise control models. Therefore, replacing the traditional PI controller in the adaptive law with Fuzzy-PI can improve the interference of MRAS identification parameters on sudden changes in speed and load torque, and improve the accuracy of parameter identification.
2、本发明辨识的d-q轴参数替代模型预测控制中恒定参数能够够获得较好的控制性能，以及增强模型预测控制的鲁棒性和提高电机的控制性能。2. The d-q axis parameters identified by the present invention replace the constant parameters in the model predictive control to obtain better control performance, enhance the robustness of the model predictive control and improve the control performance of the motor.
3、本发明将自适应律中Fuzzy-PI调节辨识到的参数使用在公式法MTPA控制中，可以自动找到最佳的MTPA操作点，在相同的转矩输出下获得相电流幅值最小，从而减小电机铜耗和提高电机运行效率。3. The present invention uses the parameters identified by the Fuzzy-PI adjustment in the adaptive law in the formula method MTPA control, which can automatically find the best MTPA operating point, and obtain the minimum phase current amplitude under the same torque output, thereby Reduce the copper consumption of the motor and improve the operating efficiency of the motor.
附图说明Description of drawings
图1：(a)五相永磁同步电机模型预测MTPA控制框图；(b)Fuzzy-PI控制下的MRAS参数辨识。Figure 1: (a) MTPA control block diagram for model prediction of five-phase permanent magnet synchronous motor; (b) MRAS parameter identification under Fuzzy-PI control.
图2：模糊逻辑成员函数和逻辑规则。(a)d-轴误差和误差变化的成员函数(b)Δk p1和Δk i1输出的成员函数(c)q-轴误差和误差变化的成员函数(d)Δk p2和Δk i2输出的成员函数(e)模糊控制规则。 Figure 2: Fuzzy logic member functions and logic rules. (a) Membership function of d-axis error and error change (b) Membership function of Δk p1 and Δk i1 output (c) Membership function of q-axis error and error change (d) Membership function of Δk p2 and Δk i2 output (e) Fuzzy control rules.
图3：MRAS在转速阶跃下的辨识的d-q轴电感。(a)L d(b)L q。 Figure 3: The identified dq-axis inductance of the MRAS under a speed step. (a) L d (b) L q .
图4：MRAS在转矩阶跃下的辨识的d-q轴电感。(a)L d(b)L q。 Figure 4: The identified dq-axis inductance of the MRAS under a torque step. (a) L d (b) L q .
图5：模型预测控制在i d＝0控制和MTPA控制对比。 Figure 5: Comparison of model predictive control at id = 0 control and MTPA control.
图6：在参数失配情况下采用和不采用参数辨识的模型预测的电流和转矩输出比较。(a) 模型预测采用参数辨识和不采用整体对比图(b)不采用参数辨识的THD含量(c)采用参数辨识的THD含量。Figure 6: Comparison of model-predicted current and torque outputs with and without parameter identification in the case of parameter mismatch. (a) Overall comparison chart of model prediction with and without parameter identification (b) THD content without parameter identification (c) THD content with parameter identification.
具体实施方式Detailed ways
本发明公开了一种采用模糊逻辑控制的永磁同步电机参数辨识的模型预测MTPA控制方法，包括如下步骤：检测电机转速，将给定转速n *与实际反馈转速n进行比较，利用PI控制器得到电机给定电流I s；再利用Fuzzy-PI调节模型参考自适应辨识出的L dq(k+1)实时更新MTPA得到参考电流i dq*，以及模型预测恒定参数L dq被L dq(k+1)替代然后，价值函数将电流预测输出的电流i p(k+2)和参考电压做差，并且根据电流最小差值来选取对应矢量的大小作为PWM生成的顺序；由逆变器生成内嵌式永磁电机的相电流I abcde和位置角度θ e；相电流经过Park变化，将自然坐标系下的相电流转换成旋转坐标系下的电流i dq；经过欧拉离散将i dq转换成电流预测模型的输入信号；PMRAS参数在线辨识的L dq(k+1)、选择矢量和离散后的电流预测模型的输入生成预测电流i p(k+2)；最后到输入信号分别为旋转坐标系d-q轴电压电流u dq，i dq和转子角速度；从而实现鲁棒性更好的模型预测MTPA控制。 The invention discloses a model predictive MTPA control method for parameter identification of permanent magnet synchronous motors controlled by fuzzy logic. Obtain the given current I s of the motor; then use the Fuzzy-PI adjustment model to refer to L dq (k+1) identified by self-adaptation to update MTPA in real time to obtain the reference current i dq *, and the model predicts the constant parameter L dq by L dq (k +1) Instead, the value function makes the difference between the current i p (k+2) output by the current prediction and the reference voltage, and selects the size of the corresponding vector according to the minimum difference of the current as the order of PWM generation; generated by the inverter The phase current I abcde and the position angle θ e of the embedded permanent magnet motor; the phase current is changed through Park, and the phase current in the natural coordinate system is converted into the current i dq in the rotating coordinate system; i dq is transformed through Euler discretization The input signal of the current prediction model; the L dq (k+1) of the PMRAS parameter online identification, the selection vector and the input of the discrete current prediction model generate the predicted current i p (k+2); finally, the input signal is the rotation Coordinate system dq-axis voltage current u dq , i dq and rotor angular velocity; thus realizing a model predictive MTPA control with better robustness.
下面将结合附图，对本发明实施例中的技术方案进行清楚、完整地描述。下面详细描述本发明的实施例，所述实施例的示例在附图中示出，其中自始至终相同或类似的标号表示相同或类似的元件或具有相同或类似功能的元件。下面通过参考附图描述的实施例是示例性的，仅用于解释本发明，而不能理解为对本发明的限制。The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Embodiments of the present invention are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals designate the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary only for explaining the present invention and should not be construed as limiting the present invention.
一种采用模糊逻辑控制的永磁同步电机d-q轴电感参数辨识的MTPA控制方法图1(a)所示。An MTPA control method for d-q axis inductance parameter identification of permanent magnet synchronous motor using fuzzy logic control is shown in Fig. 1(a).
具体实施步骤1：Specific implementation step 1:
步骤1-1，五相永磁电机的给定转速n *，与反馈转速n，相比较可以得到电机的转速误差e r，将该转速误差通过PI控制器可得到五相永磁电机的速度环误差信号I s； Step 1-1, comparing the given speed n * of the five-phase permanent magnet motor with the feedback speed n, the speed error e r of the motor can be obtained, and the speed error of the five-phase permanent magnet motor can be obtained through the PI controller ring error signal I s ;
步骤1-2，旋转坐标系d-q轴下的参考电流i d *、i q *由以下MTPA公式得到 Step 1-2, the reference current i d * and i q * under the dq axis of the rotating coordinate system are obtained by the following MTPA formula
Figure PCTCN2021100296-appb-000024
Figure PCTCN2021100296-appb-000024
Figure PCTCN2021100296-appb-000025
Figure PCTCN2021100296-appb-000025
其中：L ^ dq(k+1)是在线辨识到的d-q轴电感，由提出的模型参考自适应(Proposed model reference adaptive system，PMRAS)参数在线辨识给定； Among them: L ^ dq (k+1) is the dq-axis inductance identified online, which is given by the online identification of the parameters of the proposed model reference adaptive system (PMRAS);
步骤1-3，价值函数将电流预测输出的电流i p(k+2)和参考电流做差，并且根据做差后的电流最小差值，选取对应矢量大小作用PWM生成驱动逆变器的触发信号； Step 1-3, the value function makes a difference between the current i p (k+2) output by the current prediction and the reference current, and according to the minimum difference of the current after the difference, selects the corresponding vector size and acts on the PWM to generate the trigger for driving the inverter Signal;
步骤1-4，由PWM生成S abcde桥臂开关顺序，来驱动逆变器的开关触发顺序和脉宽大小组合生成相电流I abcde和转子位置角θ e； Steps 1-4, generate S abcde bridge arm switching sequence by PWM to drive the combination of switching trigger sequence and pulse width of the inverter to generate phase current I abcde and rotor position angle θ e ;
步骤1-5，相电流经过Park变化，将自然坐标系下的电流转换成旋转坐标系下的电流i dq； Steps 1-5, the phase current is changed through Park, and the current in the natural coordinate system is converted into the current idq in the rotating coordinate system;
步骤1-6，经过欧拉离散将i dq转换成预测模型控制的输入信号i p(k+1)； Steps 1-6, convert i dq into input signal i p (k+1) controlled by predictive model through Euler discretization;
步骤1-7，PMRAS参数在线辨识的输入信号分别为旋转坐标系d-q轴电压电流u dq，i dq和转子角速度ω m，辨识的L ^ d(k+1)和L ^ q(k+1)更新模型预测控制和MTPA中的L d和L q； Steps 1-7, the input signals for online identification of PMRAS parameters are the rotating coordinate system dq axis voltage current u dq , i dq and rotor angular velocity ω m , and the identified L ^ d (k+1) and L ^ q (k+1 ) update L d and L q in model predictive control and MTPA;
步骤1-8，PMRAS参数在线辨识过程详见第二步；For steps 1-8, see the second step for the online identification process of PMRAS parameters;
步骤1-9，电流预测通过辨识出的d-q轴电感L ^ d(k+1)和L ^ q(k+1)，选择矢量V i(i＝1,…12),离散的输入电流i p(k+1)而生成模型预测控制电流i p(k+2)； Steps 1-9, current prediction through the identified dq-axis inductance L ^ d (k+1) and L ^ q (k+1), select vector V i (i=1,...12), discrete input current i p (k+1) while generating model predictive control current i p (k+2);
Figure PCTCN2021100296-appb-000026
Figure PCTCN2021100296-appb-000026
其中，i d、i q是d-q轴电流，u d、u q是d-q轴电压，R s是定子电阻，L d、L q是d-q轴电感，L ls漏感，ω是电机中的转子角速度，ψ f是永磁磁链，T为采样周期，k为采样顺序； Among them, i d , i q are dq axis currents, u d , u q are dq axis voltages, R s is stator resistance, L d , L q are dq axis inductances, L ls leakage inductance, ω is rotor angular velocity in the motor , ψ f is the permanent magnet flux linkage, T is the sampling period, and k is the sampling sequence;
步骤1-10，经过上述步骤便可以实现模型预测控制下的MTPA控制。Steps 1-10, after the above steps, the MTPA control under the model predictive control can be realized.
将模糊逻辑控制应用在模型参考自适应中辨识d-q轴电感L ^ d(k+1)和L ^ q(k+1)，其原理如图1(b)所示。其中，具体的模糊逻辑成员函数和逻辑规则如图2所示，其中图2(a)为d-轴误差和误差变化的成员函数；2(b)Δk p1和Δk i1输出的成员函数；2(c)q-轴误差和误差变化的成员函数；2(d)Δk p2和Δk i2输出的成员函数；2(e)模糊控制规则。图2(e)E和EC分别由七个变量表示：正大表示PL，正中表示PM，正小表示PS，零表示ZO，负小表示NS，负中表示NM，负大表示NL。 Apply fuzzy logic control to model reference self-adaptation to identify dq-axis inductance L ^ d (k+1) and L ^ q (k+1), the principle of which is shown in Figure 1(b). Among them, the specific fuzzy logic membership functions and logic rules are shown in Figure 2, where Figure 2(a) is the membership function of the d-axis error and error variation; 2(b) the membership function of the output of Δk p1 and Δk i1 ; 2 (c) membership function of q-axis error and error variation; 2(d) membership function of Δk p2 and Δk i2 output; 2(e) fuzzy control rule. Figure 2(e) E and EC are respectively represented by seven variables: positive large for PL, positive middle for PM, positive small for PS, zero for ZO, negative small for NS, negative middle for NM, and negative large for NL.
具体实施步骤2：，Specific implementation step 2:,
步骤2-1，电压信号V abcde通过参考模型可得到电流信号I abcde，其中，i d、i q是d-q轴电流，u d、u q是d-q轴电压，R s是定子电阻，L d、L q是d-q轴电感，ω e是电机中的转子角速度，ψ f是转子磁链，该电流经过Park变换将五相自然坐标系下的电流转换成旋转坐标系下的电流i dq； Step 2-1, the voltage signal V abcde can obtain the current signal I abcde through the reference model, where i d , i q are the dq axis currents, u d , u q are the dq axis voltages, R s is the stator resistance, L d , L q is the inductance of the dq axis, ω e is the angular velocity of the rotor in the motor, ψ f is the flux linkage of the rotor, and the current in the five-phase natural coordinate system is transformed into the current i dq in the rotating coordinate system through Park transformation;
参考模型数学表达式：Reference model mathematical expression:
Figure PCTCN2021100296-appb-000027
Figure PCTCN2021100296-appb-000027
步骤2-2，电压信号V abcde通过Park变换将五相自然坐标系下的电压转换成旋转坐标系下的电压u dq，并作为可调模型的输入信号； Step 2-2, the voltage signal V abcde converts the voltage in the five-phase natural coordinate system into the voltage u dq in the rotating coordinate system through Park transformation, and uses it as the input signal of the adjustable model;
可调模型的数学模型：Mathematical model of the adjustable model:
Figure PCTCN2021100296-appb-000028
Figure PCTCN2021100296-appb-000028
其中，符号“^”表示需要辨识的参数或者信号；Among them, the symbol "^" indicates the parameter or signal that needs to be identified;
步骤2-3，经过参考模型的电流信号i d和i q，与可调模型电流信号i d ^和i q ^做差可得到电流误差变化e(t)的输出信号，且含有需要辨识的参数信息； In step 2-3, the current signal i d and i q of the reference model are compared with the current signal i d ^ and i q ^ of the adjustable model to obtain the output signal of the current error change e(t), which contains the need to identify Parameter information;
Figure PCTCN2021100296-appb-000029
Figure PCTCN2021100296-appb-000029
步骤2-4，比例增益Kc和微分增益Ke分别乘以电流误差，可得到模糊逻辑控制的输入电流误差(E)及其变化率(Ec)，然后将上述输入到模糊逻辑控制，具体过程详见第三部；Step 2-4, the proportional gain Kc and the differential gain Ke are multiplied by the current error respectively to obtain the input current error (E) and its change rate (Ec) of the fuzzy logic control, and then input the above to the fuzzy logic control, the specific process is detailed see part three;
步骤2-5，自适应律的比例增益K pj、积分增益K ij(j＝1,2)经过模糊逻辑控制调节，即可辨识出L ^ d(k+1)和L ^ q(k+1)，同时将该值反馈到可调模型中。 In steps 2-5, the proportional gain K pj and integral gain K ij (j=1,2) of the adaptive law are adjusted by fuzzy logic control, and L ^ d (k+1) and L ^ q (k+ 1), while feeding this value back into the adjustable model.
Figure PCTCN2021100296-appb-000030
Figure PCTCN2021100296-appb-000030
经过上述步骤1-5便可以实现提出方法的L ^ d(k+1)和L ^ q(k+1)参数辨识。 After the above steps 1-5, the parameter identification of L ^ d (k+1) and L ^ q (k+1) of the proposed method can be realized.
由图3和图4所示，本发明所提出的参数辨识方法和传统方法相比较，本发明所提出的方法更加接近参考值，具有更好的辨识精确度；图3和图4在0.1秒中转矩、转速阶跃对传统方法造成扰动，本发明所提出的方法却具有良好的鲁棒性，由此证明了所提方法的正确性。As shown in Fig. 3 and Fig. 4, compared with the traditional method, the parameter identification method proposed by the present invention is closer to the reference value and has better identification accuracy; Fig. 3 and Fig. 4 are in 0.1 second The traditional method is disturbed by medium torque and speed step, but the method proposed by the present invention has good robustness, which proves the correctness of the proposed method.
由图5可知，和i d＝0控制相比较，采用模糊逻辑控制的永磁同步电机d-q轴电感参数 应用到公式法MTPA(Formula solution MTPA,FS-MTPA)，在给定相同转矩、转速条件下，FS-MTPA控制方法输出的电流幅值更小，可以减小电机铜耗，提高运行效率。 It can be seen from Fig. 5 that compared with the i d = 0 control, the inductance parameters of the dq axis of the permanent magnet synchronous motor controlled by the fuzzy logic are applied to the formula method MTPA (Formula solution MTPA, FS-MTPA), and the same torque and speed are given Under certain conditions, the output current amplitude of the FS-MTPA control method is smaller, which can reduce the copper consumption of the motor and improve the operating efficiency.
由图6可知，在模型预测控制系统存在参数失配，本发明提出的参数辨识方法应用在模型预测控制系统中可以减小电流谐波畸变(Total harmonic distortions，THD)和转矩脉动(转矩的峰峰值)；没有参数辨识的THD和转矩脉动却较大。It can be seen from Fig. 6 that there is a parameter mismatch in the model predictive control system, and the parameter identification method proposed by the present invention is applied in the model predictive control system to reduce current harmonic distortion (Total harmonic distortions, THD) and torque ripple (torque peak-to-peak value); THD and torque ripple without parameter identification are relatively large.
在本说明书的描述中，参考术语“一个实施例”、“一些实施例”、“示意性实施例”、“示例”、“具体示例”、或“一些示例”等的描述意指结合该实施例或示例描述的具体特征、结构、材料或者特点包含于本发明的至少一个实施例或示例中。在本说明书中，对上述术语的示意性表述不一定指的是相同的实施例或示例。而且，描述的具体特征、结构、材料或者特点可以在任何的一个或多个实施例或示例中以合适的方式结合。In the description of this specification, references to the terms "one embodiment," "some embodiments," "exemplary embodiments," "example," "specific examples," or "some examples" are intended to mean that the implementation A specific feature, structure, material, or characteristic described by an embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
尽管已经示出和描述了本发明的实施例，本领域的普通技术人员可以理解：在不脱离本发明的原理和宗旨的情况下可以对这些实施例进行多种变化、修改、替换和变型，本发明的范围由权利要求及其等同物限定。Although the embodiments of the present invention have been shown and described, those skilled in the art can understand that various changes, modifications, substitutions and modifications can be made to these embodiments without departing from the principle and spirit of the present invention. The scope of the invention is defined by the claims and their equivalents.

Claims (6)

  1. 一种采用模糊逻辑控制的永磁同步电机d-q轴电感参数辨识的MTPA控制方法，其特征在于，包括如下步骤：An MTPA control method for d-q axis inductance parameter identification of a permanent magnet synchronous motor using fuzzy logic control, characterized in that it comprises the following steps:
    步骤1，首先推导出公式法的MTPA电流角计算公式，当电机参数实时更新时候实现永磁电机的MTPA控制，以及推导出永磁同步电机离散化的模型预测控制方程，实现对该电机的预测电流控制；Step 1, first deduce the MTPA current angle calculation formula of the formula method, realize the MTPA control of the permanent magnet motor when the motor parameters are updated in real time, and derive the model predictive control equation for the discretization of the permanent magnet synchronous motor to realize the prediction of the motor current control;
    步骤2，根据Popov超稳定性理论原理，并应用在模型参考自适应参数辨识方法中，然后，推导出了永磁同步电机的d-q轴电感辨识自适应率表达式，当辨识到的d-q轴电感不断反馈到可调模型直到与参考模型误差几乎为零，则得到辨识的d-q轴电感参数；Step 2. According to the principle of Popov’s ultra-stability theory, and applied in the model reference adaptive parameter identification method, then, the d-q axis inductance identification adaptive rate expression of the permanent magnet synchronous motor is deduced. When the identified d-q axis inductance Continuously feed back to the adjustable model until the error with the reference model is almost zero, then the identified d-q axis inductance parameters are obtained;
    步骤3，将模糊逻辑控制应用在模型参考自适应中，推导出自适应律的比例、积分增益调整因子，并与模型参考自适应中的比例、积分增益共同调节d-q轴电感，则可得改进模型参考自适应辨识的d-q轴电感参数；Step 3: Apply fuzzy logic control to model reference adaptation, derive the proportional and integral gain adjustment factors of the adaptive law, and adjust the d-q axis inductance together with the proportional and integral gain in model reference adaptation, then an improved model can be obtained Refer to the d-q axis inductance parameters of adaptive identification;
    步骤4，将改进模型参考自适应辨识的d-q轴电感参数使用到MTPA电流角计算公式和预测电流控制中，以替代原有的固定参数和实时更新，实现永磁电机的MTPA控制和消除电流预测控制中电感失配带来的影响，从而能提高永磁同步电机的运行效率和增大控制系统参数鲁棒性。Step 4: Use the d-q axis inductance parameters of the improved model reference adaptive identification into the MTPA current angle calculation formula and predictive current control to replace the original fixed parameters and update them in real time to achieve MTPA control and eliminate current prediction for permanent magnet motors The impact of inductance mismatch in the control can improve the operating efficiency of the permanent magnet synchronous motor and increase the robustness of the control system parameters.
  2. 根据权利要求1所述的一种采用模糊逻辑控制的永磁同步电机d-q轴电感参数辨识的MTPA控制方法，其特征在于，所述步骤1的具体过程为：The MTPA control method for d-q axis inductance parameter identification of a permanent magnet synchronous motor using fuzzy logic control according to claim 1, wherein the specific process of step 1 is:
    步骤1-1，五相永磁电机的给定转速n *，与反馈转速n，相比较得到电机的转速误差e r，将该转速误差通过PI控制器可得到五相永磁电机的速度环误差信号I s； Step 1-1, compare the given speed n * of the five-phase permanent magnet motor with the feedback speed n to obtain the speed error e r of the motor, and pass the speed error through the PI controller to obtain the speed loop of the five-phase permanent magnet motor error signal I s ;
    步骤1-2，旋转坐标系d-q轴下的参考电流i d *、i q *由以下MTPA电流角公式γ MTPA计算得到； Step 1-2, the reference current i d * and i q * under the dq axis of the rotating coordinate system are calculated by the following MTPA current angle formula γ MTPA ;
    Figure PCTCN2021100296-appb-100001
    Figure PCTCN2021100296-appb-100001
    Figure PCTCN2021100296-appb-100002
    Figure PCTCN2021100296-appb-100002
    其中：L ^ d(k+1)、L ^ q(k+1)分别是在线辨识到d-q轴电感参数，是由提出的模型参考自适应(PMRAS)参数在线辨识给定；ψ f是永磁磁链参数；I s是转速环PI控制器输出； Among them: L ^ d (k+1), L ^ q (k+1) are the dq axis inductance parameters identified online respectively, which are given by the online identification of the proposed model reference adaptive (PMRAS) parameters; ψ f is the permanent Magnetic flux linkage parameter; I s is the output of the speed loop PI controller;
    步骤1-3，价值函数将电流预测输出的电流i p(k+2)和参考电流做差，并且根据做差后的 电流最小差值，选取对应矢量大小作用PWM生成驱动逆变器的触发信号； Step 1-3, the value function makes a difference between the current i p (k+2) output by the current prediction and the reference current, and according to the minimum difference of the current after the difference, selects the corresponding vector size and acts on the PWM to generate the trigger for driving the inverter Signal;
    步骤1-4，由PWM生成S abcde桥臂开关顺序，来驱动逆变器的开关触发顺序和脉宽大小组合生成相电流I abcde和转子位置角θ e； Steps 1-4, generate S abcde bridge arm switching sequence by PWM to drive the combination of switching trigger sequence and pulse width of the inverter to generate phase current I abcde and rotor position angle θ e ;
    步骤1-5，相电流经过Park变化，将自然坐标系下的电流I abcde转换成旋转坐标系下的电流i dq； Step 1-5, the phase current is changed through Park, and the current I abcde in the natural coordinate system is converted into the current i dq in the rotating coordinate system;
    步骤1-6，i dq经过欧拉离散转换成预测模型控制的输入信号i p(k+1)； Steps 1-6, i dq is transformed into the input signal i p (k+1) controlled by the predictive model through Euler discrete transformation;
    步骤1-7，PMRAS参数在线辨识的输入信号分别为旋转坐标系d-q轴电压电流u dq，i dq和转子角速度ω m，辨识的L ^ d(k+1)和L ^ q(k+1)更新模型预测控制和MTPA中的L d和L q； Steps 1-7, the input signals for online identification of PMRAS parameters are the rotating coordinate system dq axis voltage current u dq , i dq and rotor angular velocity ω m , and the identified L ^ d (k+1) and L ^ q (k+1 ) update L d and L q in model predictive control and MTPA;
    步骤1-8，电流预测通过辨识出的d-q轴电感L ^ d(k+1)和L ^ q(k+1)，选择矢量V i(i＝1,…12),离散的输入电流i p(k+1)而生成模型预测控制电流i p(k+2)； Steps 1-8, current prediction through the identified dq-axis inductance L ^ d (k+1) and L ^ q (k+1), select vector V i (i=1,...12), discrete input current i p (k+1) while generating model predictive control current i p (k+2);
    Figure PCTCN2021100296-appb-100003
    Figure PCTCN2021100296-appb-100003
    其中，i d、i q是d-q轴电流，u d、u q是d-q轴电压，i x、i y是三次空间x-y轴电流，u x、u y是三次空间x-y轴电压，R s是定子电阻，L ls漏感，ω是电机中的转子角速度，ψ f是永磁磁链，T为采样周期，k为采样顺序；L ^ d(k+1)、L ^ q(k+1)分别是在线辨识到d-q轴电感参数，是由提出的模型参考自适应(PMRAS)参数在线辨识给定； Among them, i d , i q are the dq axis currents, u d , u q are the dq axis voltages, ix , i y are the xy axis currents of the third space, u x , u y are the xy axis voltages of the third space, R s is the stator Resistance, L ls leakage inductance, ω is the rotor angular velocity in the motor, ψ f is the permanent magnet flux linkage, T is the sampling period, k is the sampling sequence; L ^ d (k+1), L ^ q (k+1) Respectively, the dq axis inductance parameters are identified online, which are given by the proposed model reference adaptive (PMRAS) parameter online identification;
    经过上述步骤便可以实现模型预测控制下的MTPA控制。After the above steps, the MTPA control under the model predictive control can be realized.
  3. 根据权利要求1所述的一种采用模糊逻辑控制的永磁同步电机d-q轴电感参数辨识的MTPA控制方法，其特征在于，所述步骤2的具体过程为：A kind of MTPA control method that adopts fuzzy logic control d-q axis inductance parameter identification of permanent magnet synchronous motor according to claim 1, it is characterized in that, the specific process of described step 2 is:
    步骤2-1，电压信号V abcde通过参考模型可得到电流信号I abcde，其中，i d、i q是d-q轴电流，u d、u q是d-q轴电压，R s是定子电阻，L d、L q是d-q轴电感，ω m是电机中的转子角速度，ψ f是转子磁链，该电流经过Park变换将五相自然坐标系下的电流转换成旋转坐标系下的电流i dq； Step 2-1, the voltage signal V abcde can obtain the current signal I abcde through the reference model, where i d , i q are the dq axis currents, u d , u q are the dq axis voltages, R s is the stator resistance, L d , L q is the inductance of the dq axis, ω m is the angular velocity of the rotor in the motor, ψ f is the flux linkage of the rotor, the current in the five-phase natural coordinate system is converted into the current i dq in the rotating coordinate system through Park transformation;
    模型参考自适应的参考模型方程表示为：The reference model equation for model reference adaptation is expressed as:
    Figure PCTCN2021100296-appb-100004
    Figure PCTCN2021100296-appb-100004
    步骤2-2，电压信号V abcde通过Park变换将五相自然坐标系下的电压转换成旋转坐标系下的电压u dq，并作为可调模型的输入信号； Step 2-2, the voltage signal V abcde converts the voltage in the five-phase natural coordinate system into the voltage u dq in the rotating coordinate system through Park transformation, and uses it as the input signal of the adjustable model;
    模型参考自适应的可调模型方程表示为：The adjustable model equation for model reference adaptation is expressed as:
    Figure PCTCN2021100296-appb-100005
    Figure PCTCN2021100296-appb-100005
    其中，符号“^”表示需要辨识的参数或者信号；Among them, the symbol "^" indicates the parameter or signal that needs to be identified;
    步骤2-3，经过参考模型的电流信号i d和i q，与可调模型电流信号i d ^和i q ^做差可得到电流误差变化e(t)的输出信号，且含有需要辨识的参数信息； In step 2-3, the current signal i d and i q of the reference model are compared with the current signal i d ^ and i q ^ of the adjustable model to obtain the output signal of the current error change e(t), which contains the need to identify Parameter information;
    Figure PCTCN2021100296-appb-100006
    Figure PCTCN2021100296-appb-100006
  4. 根据权利要求3所述的一种采用模糊逻辑控制的永磁同步电机d-q轴电感参数辨识的MTPA控制方法，其特征在于，步骤3的具体过程为：The MTPA control method for d-q axis inductance parameter identification of a permanent magnet synchronous motor using fuzzy logic control according to claim 3, wherein the specific process of step 3 is:
    步骤3-1，为了简洁地描述方程，分别将步骤2-1的参考模型公式和步骤2-2的可调模型公式，用以下标准形式重写为：In step 3-1, in order to describe the equations concisely, the reference model formula in step 2-1 and the adjustable model formula in step 2-2 are respectively rewritten in the following standard form:
    Figure PCTCN2021100296-appb-100007
    Figure PCTCN2021100296-appb-100007
    其中，in,
    Figure PCTCN2021100296-appb-100008
    Figure PCTCN2021100296-appb-100008
    上述符号“.”表示差分运算符，根据标准形式建立可调模型，除d-q轴电感和电流外，所有参数与参考模型完全一致，辨识值用符号“^”表示；式中u dq为定子轴电压,i dq为定子轴电流，R s为定子相电阻,ω e是电角速度，ψ f是电机永磁体磁链； The above symbol "." indicates a differential operator. The adjustable model is established according to the standard form. Except for the inductance and current of the dq axis, all parameters are completely consistent with the reference model. The identification value is represented by the symbol "^"; where u dq is the stator axis Voltage, i dq is the stator shaft current, R s is the stator phase resistance, ω e is the electrical angular velocity, ψ f is the flux linkage of the permanent magnet of the motor;
    步骤3-2，把可调模型改写如下：Step 3-2, rewrite the adjustable model as follows:
    Figure PCTCN2021100296-appb-100009
    Figure PCTCN2021100296-appb-100009
    其中,
    Figure PCTCN2021100296-appb-100010
    in,
    Figure PCTCN2021100296-appb-100010
    步骤3-3，根据Popov超稳定性理论的原理，如果反馈系统要保持稳定，则非线性回路应满足以下公式Step 3-3, according to the principle of Popov's ultra-stability theory, if the feedback system is to remain stable, the nonlinear loop should satisfy the following formula
    Figure PCTCN2021100296-appb-100011
    Figure PCTCN2021100296-appb-100011
    且有and have
    Figure PCTCN2021100296-appb-100012
    Figure PCTCN2021100296-appb-100012
    其中，η(0,t)为积分函数，r 2为一个不依赖积分上限t的有限正常量，W是非线性反馈输入，w1为中间变量且w1＝-W； Wherein, η (0, t) is an integral function, r 2 is a finite normal quantity independent of the integral upper limit t, W is a nonlinear feedback input, w1 is an intermediate variable and w1=-W;
    参数自适应律的设计目的是为了在线估计出需要的参数，然后通过反馈调节使得控制系统的广义误差逐渐趋向于零；The design purpose of the parameter adaptive law is to estimate the required parameters online, and then make the generalized error of the control system gradually tend to zero through feedback adjustment;
    首先，分析直轴电感辨识的自适应规律，并给出了相应的计算公式，交轴自适应律的推导过程与其相似，在此略去；Firstly, the adaptive law of direct-axis inductance identification is analyzed, and the corresponding calculation formula is given. The derivation process of quadrature-axis adaptive law is similar to it, so it is omitted here;
    Figure PCTCN2021100296-appb-100013
    Figure PCTCN2021100296-appb-100013
    其中，L ^ d为需辨识的直轴电感参数，L d为参考直轴电感参数，R 1(τ)是关于τ的积分函数，R 2(τ)是关于τ的函数； Among them, L ^ d is the direct-axis inductance parameter to be identified, L d is the reference direct-axis inductance parameter, R 1 (τ) is the integral function of τ, and R 2 (τ) is the function of τ;
    然后，将上式代入步骤3-3方程，可得Then, substituting the above formula into the equation of step 3-3, we can get
    Figure PCTCN2021100296-appb-100014
    Figure PCTCN2021100296-appb-100014
    其中，符号“.”表示差分运算符，ε为参考模型与可调模型电流输出差，ε 1为参考模型与可调模型直轴电流输出差； Among them, the symbol "." represents the differential operator, ε is the current output difference between the reference model and the adjustable model, and ε1 is the direct-axis current output difference between the reference model and the adjustable model;
    用以下定理求解上面公式，Solve the above formula using the following theorem,
    Figure PCTCN2021100296-appb-100015
    Figure PCTCN2021100296-appb-100015
    且正实常数k大于0，f(t)是关于t的可积函数，And the positive real constant k is greater than 0, f(t) is an integrable function about t,
    设以下式子为Let the following formula be
    Figure PCTCN2021100296-appb-100016
    Figure PCTCN2021100296-appb-100016
    则可得到R 1(τ)＝K i1ε 1u d，只要K p1大于0，则R 2(τ)＝K p1ε 1u d，则自适应律有如下式： Then R 1 (τ)=K i1 ε 1 u d can be obtained, as long as K p1 is greater than 0, then R 2 (τ)=K p1 ε 1 u d , then the adaptive law has the following formula:
    Figure PCTCN2021100296-appb-100017
    Figure PCTCN2021100296-appb-100017
    其中，K p1、K i1分别为直轴电感参数L ^ d自适应率的比例积分增益，K p2、K i2分别为交轴电感参数L ^ q自适应率的比例积分增益。 Among them, K p1 and K i1 are the proportional integral gains of the direct-axis inductance parameter L ^ d adaptation rate, and K p2 and K i2 are the proportional integral gains of the quadrature-axis inductance parameter L ^ q adaptation rate respectively.
  5. 根据权利要求4所述的一种采用模糊逻辑控制的永磁同步电机d-q轴电感参数辨识的MTPA控制方法，其特征在于，所述自适应律的设计采用比例积分的调节原理，根据超稳态定律为了满足模型参考自适应控制系统超稳定，不仅要满足线性定常前向通道严格正实，而且非线性反馈回路满足积分不等式，因此省略了一系列的推导过程，得到电机参数的自适应律表达式子。According to the MTPA control method of d-q axis inductance parameter identification of a permanent magnet synchronous motor using fuzzy logic control according to claim 4, it is characterized in that the design of the adaptive law adopts the adjustment principle of proportional integral, according to the ultra-stable state In order to meet the super stability of the model reference adaptive control system, the law must not only satisfy the strict positive reality of the linear steady forward channel, but also satisfy the integral inequality in the nonlinear feedback loop, so a series of derivation processes are omitted, and the expression of the adaptive law of the motor parameters is obtained formula.
  6. 根据权利要求1所述的一种采用模糊逻辑控制的永磁同步电机d-q轴电感参数辨识的MTPA控制方法，其特征在于，所述步骤4的具体过程为：A kind of MTPA control method that adopts fuzzy logic control d-q axis inductance parameter identification of permanent magnet synchronous motor according to claim 1, it is characterized in that, the specific process of described step 4 is:
    步骤4-1，经过参考模型的电流信号i d和i q，与可调模型电流信号i d ^和i q ^做差可得到电流误差变化e(t)的输出信号，且含有需要辨识的参数信息； Step 4-1, the current signal i d and i q of the reference model, and the current signal i d ^ and i q ^ of the adjustable model are different to obtain the output signal of the current error change e(t), and it contains the need to identify Parameter information;
    Figure PCTCN2021100296-appb-100018
    Figure PCTCN2021100296-appb-100018
    步骤4-2，比例增益Kc和微分增益Ke分别乘以电流误差，可得到模糊逻辑控制的输入电流误差E及其变化率Ec，然后将上述输入到模糊逻辑控制；Step 4-2, the proportional gain Kc and the differential gain Ke are respectively multiplied by the current error to obtain the input current error E and its rate of change Ec of the fuzzy logic control, and then input the above to the fuzzy logic control;
    步骤4-3，解模糊，通过重心解模糊技术将隐式模糊控制集转化为显式输出，模糊推理采用直积法，Step 4-3, defuzzification, convert the implicit fuzzy control set into explicit output through the center of gravity defuzzification technology, fuzzy reasoning adopts the direct product method,
    Figure PCTCN2021100296-appb-100019
    Figure PCTCN2021100296-appb-100019
    其中u ei(e)，u ei(de/dt)分别是当前误差和变化当前误差的等级； Among them, u ei (e), u ei (de/dt) are the current error and the level of changing the current error respectively;
    步骤4-4，因此从上述产品操作中导出第i条控制规则，Step 4-4, thus deriving the i-th control rule from the above product operation,
    uΔL nRule i(ΔL s)＝sup[a iuΔL′ n(ΔL s)] uΔL n Rule i (ΔL s )=sup[a i uΔL′ n (ΔL s )]
    其中，隶属度为u，控制器输出为ΔL n，Rule为模糊推理规则，uΔL nRule i(ΔL s)表示控制器输出ΔL s的第i条控制规则中的控制决策隶属度，uΔL′ n(ΔL s)为控制器输出ΔL s在论域中模糊集合ΔL′ n的隶属度函数，ΔL s为隶属度函数； Among them, the membership degree is u, the controller output is ΔL n , Rule is the fuzzy inference rule, uΔL n Rule i (ΔL s ) represents the control decision membership degree in the i-th control rule of the controller output ΔL s , uΔL′ n (ΔL s ) is the membership function of the controller output ΔL s in the fuzzy set ΔL′ n in the domain of discourse, and ΔL s is the membership function;
    步骤4-5，控制器输出ΔL n的隶属函数uΔL n为； Step 4-5, the membership function uΔL n of the controller output ΔL n is;
    Figure PCTCN2021100296-appb-100020
    Figure PCTCN2021100296-appb-100020
    步骤4-6，最后采用重心法进行解模糊，在离散域中可以得到输出的精确值；Steps 4-6, finally use the center of gravity method to defuzzify, and the precise value of the output can be obtained in the discrete domain;
    Figure PCTCN2021100296-appb-100021
    Figure PCTCN2021100296-appb-100021
    其中，ΔZ s为模糊控制在离散域上的具体输出值，uΔL nΔL si为离散域上第i条控制规则中的隶属度系数，C(ΔL si)为第i条控制规则中对应的隶属度函数； Among them, ΔZ s is the specific output value of fuzzy control in the discrete domain, uΔL n ΔL si is the membership degree coefficient in the i-th control rule in the discrete domain, and C(ΔL si ) is the corresponding membership in the i-th control rule degree function;
    步骤4-7，将输出误差与电流误差相乘得到ΔK p和ΔK i，并通过K u持续调整输出， Steps 4-7, multiply the output error by the current error to obtain ΔK p and ΔK i , and continuously adjust the output through K u ,
    Figure PCTCN2021100296-appb-100022
    Figure PCTCN2021100296-appb-100022
    Figure PCTCN2021100296-appb-100023
    Figure PCTCN2021100296-appb-100023
    其中，ΔK p和ΔK i分别模糊输出的的比例、积分增益调整因子，K u为调整输出标度增益； Among them, ΔK p and ΔK i are the proportional and integral gain adjustment factors of the fuzzy output respectively, and K u is the adjustment output scale gain;
    步骤4-8，将ΔK p和ΔK i分别与模型参考自适应中的比例、积分增益共同调节d-q轴电感，则可得改进模型参考自适应辨识的d-q轴电感参数。 In steps 4-8, ΔK p and ΔK i are used to adjust the dq-axis inductance together with the proportional and integral gains in the model reference adaptation, and then the dq-axis inductance parameters for improved model reference adaptive identification can be obtained.
    Figure PCTCN2021100296-appb-100024
    Figure PCTCN2021100296-appb-100024
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