Motor modeling and non-cascade servo control method and system based on T-S fuzzy

Abstract

The invention discloses a motor modeling and non-cascade servo control method and system based on T-S fuzzy. The method comprises the following steps: establishing a mathematical model on a two-phase synchronous rotation orthogonal coordinate system to obtain state space description; obtaining a T-S fuzzy model and a closed-loop system according to the state space description; and for the closed-loop system, determining the gain of the non-cascade servo controller based on the Lyapunov stability theory. According to the invention, the control performance and the anti-interference capability of the driving system are effectively improved, and parameter adjustment and physical realization are easy.

Description

Technical Field

[0001] The present invention belongs to the technical field of motor control, and in particular relates to a motor modeling and non-cascade servo control method and system based on TS fuzzy. Background Art

[0002] In recent years, with the advancement of power electronics, automatic control, and computer technologies, AC drive control systems have gradually replaced DC speed control systems in the field of high-performance speed regulation. Permanent magnet synchronous motors (PMSMs) have dominated AC speed regulation systems due to their high power density, large torque-to-inertia ratio, and fast dynamic response. However, the uncertainty of the motor's internal parameters and the variability of external load disturbances significantly degrade the control performance of PMSM servo systems. Therefore, research on control technologies for PMSM drive systems has important theoretical and engineering significance.

[0003] With the continuous development and improvement of control theory, many nonlinear control techniques have been applied to permanent magnet synchronous motor drive systems. By performing an arctan(·) operation on the speed deviation and using its value as a weighting factor, a composite speed regulator based on PI (Proportional Integral) and sliding mode control is proposed, improving the performance of the control system. For the low-speed, high-torque permanent magnet synchronous motor used in mining scraper conveyors, composite nonlinear feedback technology is introduced into the design of an integral sliding mode control speed regulation system. The proposed composite sliding mode controller effectively resolves the conflict between overshoot and rapidity in traditional control strategies. Considering that the electrical time constant of a permanent magnet synchronous motor is much smaller than the mechanical time constant, it can be modeled as a dual-time-scale system, and a non-cascaded sliding mode controller is designed based on this. Furthermore, the current response of a permanent magnet synchronous motor is much faster than the mechanical dynamics. Therefore, sliding mode controllers can be designed separately for the fast and slow time scales, ultimately forming a new composite speed and current regulator. Based on the modeling technology of the TS (Takagi-Sugeno) fuzzy model, a non-cascade sliding mode controller with a disturbance observer is proposed. The effectiveness of the proposed method is verified using a permanent magnet synchronous motor. In summary, the non-cascade control strategy, as a new vector control structure, can simplify the system control framework and achieve simultaneous speed and current regulation of the permanent magnet synchronous motor. Summary of the Invention

[0004] The purpose of the present invention is to provide a motor modeling and non-cascade servo control method and system based on TS fuzzy, which effectively improves the control performance and anti-interference ability of the drive system and effectively avoids possible pathological numerical problems.

[0005] To achieve the purpose of the present invention, on the one hand, the present invention provides a motor modeling and non-cascade servo control method based on TS fuzzy, comprising the following steps:

[0006] Step 1: Establish a mathematical model in a two-phase synchronous rotating orthogonal coordinate system to obtain a state space description;

[0007] Step 1-1: For the dynamic model of the motor in a three-phase stationary symmetric coordinate system, introduce Clarke transform and Park transform to reduce the order and decouple it. Then, through coordinate transformation, obtain the mathematical model of the motor in a two-phase synchronous rotating orthogonal dq coordinate system.

[0008] Step 1-2: Introduce state variables to achieve tracking control objectives;

[0009] Steps 1-3, define the output equation;

[0010] Steps 1-4: Define singular perturbation parameters;

[0011] Step 1-5: Combine steps 1-1 to 1-4 to obtain a state space description of the motor singular perturbation model.

[0012] Step 2: According to the state space description, a TS fuzzy model and a closed-loop system are obtained;

[0013] Step 2-1: To characterize the nonlinear characteristics of the system matrix A, the electrical angular velocity ω e as antecedent variables;

[0014] Step 2-2: defining the standardized membership functions of two fuzzy rules for the antecedent variables;

[0015] Step 2-3: Based on the antecedent variables, the nonlinear motor singular perturbation model is equivalent to a TS fuzzy model using the IF-THEN rule;

[0016] Step 2-4, using the standardized membership function and the TS fuzzy model to obtain a defuzzified model;

[0017] Step 2-5: Design a defuzzified non-cascade servo controller based on the parallel distributed compensation technology;

[0018] Step 2-6: Following the fuzzy rules, the defuzzified model and the defuzzified non-cascade servo controller are combined to obtain a closed-loop system.

[0019] Step 3: For the closed-loop system, determine the gain of the non-cascade servo controller based on Lyapunov stability theory;

[0020] Step 3-1: Given a scalar Set the intermediate variable matrix Zk k = 1, 2, …, 5; and let it satisfy the linear matrix inequality;

[0021] Step 3-2, for any determine the relationship of the singular perturbation matrix and the variable matrix dependent on ε;

[0022] Step 3-3, given scalar γ > 0 and Let the intermediate variable matrix Z k and the intermediate gain matrix G j j = 1, 2; satisfy the linear matrix inequality;

[0023] Step 3-4, for the arbitrary determine the controller gain matrix;

[0024] Step 3-5, through the above steps, the closed-loop system is exponentially stable, and can satisfy the H ∞ performance under zero initial conditions.

[0025] In another aspect, the present application also provides a system of motor modeling and non-cascaded servo control method based on T-S fuzzy, comprising the following modules:

[0026] A state vector synthesis module is configured to select the current signals of the d-axis and q-axis of a two-phase synchronous rotating orthogonal coordinate system and the angular velocity as state variables; in addition, based on the deviation between the angular velocity given signal and the angular velocity, the angular velocity given signal is integrated to obtain a new state variable, and the angular velocity given signal and the new state variable are collectively composed into a state vector, which is sent to a non-cascaded servo controller module;

[0027] The non-cascaded servo controller module is configured to calculate a control vector according to the state vector, the membership function of the controller, and the controller gain matrix dependent on the singular perturbation parameter, and send the control vector to a T-S fuzzy singular perturbation system module;

[0028] A system disturbance synthesis module is configured to collectively compose the angular velocity given signal and the load torque disturbance, and input the system disturbance to the T-S fuzzy singular perturbation system module as a system disturbance;

[0029] A T-S fuzzy equation module is configured to calculate the response values of the angular velocity and the current signals of the d-axis and q-axis according to the input voltage control signals of the d-axis and q-axis of the two-phase synchronous rotating orthogonal coordinate system, the system disturbance, the singular perturbation matrix, the membership function of the model, the system matrix, the control matrix, and the disturbance matrix, and feed back the response values to the state vector synthesis module and output the response values as control system output signals.

[0030] Compared with the prior art, the significant progress of the present application is that: (1) the present application takes the surface-mounted permanent magnet synchronous motor as the research object, and considers that the rate of change of the mechanical speed is much lower than the response dynamics of the current, which is a typical double-time scale system. Therefore, the mathematical model of the singular perturbation system is established based on the electrical time constant;

[0031] (2) the present application introduces the IF-THEN rule to obtain the T-S fuzzy model, and designs a non-cascaded servo controller by using the parallel distributed compensation technology;

[0032] (3) the gain design of the controller of the present application depends on the singular perturbation parameter, which is different from the controller design of the conventional system, and effectively avoids the possible ill-conditioned numerical problems;

[0033] (4) the gain value of the non-cascaded servo controller is determined according to the linear matrix inequality in the present application, and the H ∞ performance of the closed-loop system is obtained by using the Lyapunov stability theory, which effectively improves the control performance and anti-interference ability of the permanent magnet synchronous motor driving system.

[0034] In order to more clearly illustrate the functional characteristics and structural parameters of the present application, the following further describes the present application in conjunction with the accompanying drawings and specific embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0035] The drawings described herein are used to provide further understanding of the present application, and form a part of the present application. The schematic embodiments of the present application and their descriptions are used to explain the present application, and do not constitute an improper limitation on the present application. In the drawings:

[0036] Figure 1 is a system flowchart of the present application. DETAILED DESCRIPTION

[0037] The technical solutions in the embodiments of the present application will be described clearly and completely in conjunction with the drawings of the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0038] The motor modeling and non-cascaded servo control method based on T-S fuzzy of the present application combines Figure 1 , comprising the following steps:

[0039] Step 1, a mathematical model is established on the two-phase synchronous rotating orthogonal coordinate system to obtain a state space description;

[0040] Step 2, according to the state space description, a T-S fuzzy model and a closed-loop system are obtained;

[0041] Step 3: For the closed-loop system, based on Lyapunov stability theory, determine the gain of the non-cascade servo controller.

[0042] The step 1 specifically includes the following steps:

[0043] Step 1-1: The Clark transform and Park transform are introduced into the dynamic model of the motor in the three-phase stationary symmetric coordinate system to reduce the order and decouple it. Then, through coordinate transformation, the mathematical model of the motor in the two-phase synchronous rotating orthogonal dq coordinate system is obtained;

[0044] Step 1-2: Introduce state variables to achieve tracking control objectives;

[0045] Steps 1-3, define the output equation;

[0046] Steps 1-4: Define singular perturbation parameters;

[0047] Step 1-5: Combine steps 1-1 to 1-4 to obtain a state space description of the motor singular perturbation model.

[0048] The step 1 is specifically shown in the following formula:

[0049] Step 1-1 consists of the electrical dynamics and mechanical equations:

[0050]

[0051] Among them, L s and R s are the inductance and resistance of the stator winding respectively; d / dt is the differential operator; i d and i q are the currents of the d-axis and q-axis stator windings in the two-phase synchronous rotating orthogonal dq coordinate system respectively; ψ f is the permanent magnet flux; u d and u q are the voltages of the d-axis and q-axis stator windings respectively; ω e is the electrical angular velocity;

[0052] Correspondingly, the mechanical dynamic equation is:

[0053]

[0054] Where J is the moment of inertia; K T =3p n ψ f / 2 is the electromagnetic torque coefficient, p n is the pole pair number; F is the viscous friction coefficient; T m is the load torque disturbance; ω m is the mechanical angular velocity, and satisfies ωe =p n ω m ;

[0055] Based on ω e =p n ω m , and the following formula is obtained:

[0056]

[0057] The steps 1-2 are shown below:

[0058]

[0059] Where e is the tracking error variable; is the first-order differential of the tracking error variable; is the given signal of angular velocity;

[0060] The steps 1-3 are shown below:

[0061] y=ω e ;

[0062] Among them, y is the output variable;

[0063] The steps 1-4 are shown below:

[0064]

[0065] Among them, ε is the singular perturbation parameter;

[0066] The steps 1-5 are shown below:

[0067]

[0068] in, is the singular perturbation model of the motor; E(ε) is the singular perturbation matrix; x=[ω e ei d i q ] T is the state vector, and the superscript symbol T represents transpose; is the first-order differential of the state vector; A is the nonlinear system matrix; B is the control matrix; u=[u d u q ] T is the control vector; D is the perturbation matrix; is interference; C = [10 0 0] is the output matrix;

[0069] The definitions of the correlation matrices in steps 1-5 are as follows:

[0070]

[0071] The step 2 specifically includes the following steps:

[0072] Step 2-1: To characterize the nonlinear characteristics of the system matrix A, the electrical angular velocity ω e as antecedent variables;

[0073] Step 2-2: defining the standardized membership functions of two fuzzy rules for the antecedent variables;

[0074] Step 2-3: Based on the antecedent variables, the nonlinear motor singular perturbation model is equivalent to a TS fuzzy model using the IF-THEN rule;

[0075] Step 2-4, using the standardized membership function and the TS fuzzy model to obtain a defuzzified model;

[0076] Step 2-5: Design a defuzzified non-cascade servo controller based on the parallel distributed compensation technology;

[0077] Step 2-6: Following the fuzzy rules, the defuzzified model and the defuzzified non-cascade servo controller are combined to obtain a closed-loop system.

[0078] The step 2 is shown in the following formula:

[0079] The prerequisite variable v of step 2-1 is:

[0080] v=ω e ∈[w W];

[0081] Where w and W are the electrical angular velocity ω e the minimum and maximum values ​​of

[0082] The standardized membership function of step 2-2 is:

[0083]

[0084] Among them, μ1=μ1(v) and μ2=μ2(v) are the standardized membership functions corresponding to the two fuzzy rules, and always satisfy the constraint relationship: μ1+μ2=1;

[0085] The TS fuzzy model in steps 2-3 is composed of several linear subsystems:

[0086]

[0087] Among them, i is the rule variable of the model; M i is the fuzzy set corresponding to the fuzzy rule; A i is the linear system matrix corresponding to the i-th rule; r is the number of fuzzy rules, r = 2;

[0088] The defuzzification model of steps 2-4 is shown below:

[0089]

[0090] Among them, μ i is the membership function of the model;

[0091] Non-cascaded servo controller for steps 2-5:

[0092]

[0093] Among them, j is the rule variable of the controller; K j (ε) is the controller gain matrix to be determined;

[0094] Defuzzified non-cascaded servo controller:

[0095]

[0096] Among them, μ j is the membership function of the controller;

[0097] The closed loop system of steps 2-6:

[0098]

[0099] The step 3 specifically includes the following steps:

[0100] Step 3-1: Given a scalar Set the intermediate variable matrix Z k , k=1, 2, …, 5; and let it satisfy the linear matrix inequality;

[0101] Step 3-2: For any Determining a relationship between the singular perturbation matrix and the variable matrix dependent on ε;

[0102] Step 3-3, given scalar γ>0 and Let the intermediate variable matrix Z k And the intermediate gain matrix G j , j=1,2; satisfying the linear matrix inequality;

[0103] Step 3-4: For any Determine the controller gain matrix;

[0104] Step 3-5: Determine through the above steps that the closed-loop system is exponentially stable and can satisfy H under zero initial conditions. ∞ performance.

[0105] The step 3 is specifically shown in the following formula:

[0106] The step 3-1 is shown in the following formula:

[0107] Z1>0;

[0108]

[0109] in, is the upper bound of the singular perturbation parameter ε; Z k is the intermediate variable matrix and satisfies The symbol * represents an induced symmetric matrix block; 0 represents a zero matrix with an appropriate dimension; the step 3-2 is as follows:

[0110] E(ε)Z(ε)=Z T (ε)E(ε)>0;

[0111] The Z(ε) is a variable matrix that depends on ε, and its definition is as follows:

[0112]

[0113] The linear matrix inequality satisfied by step 3-3 is as follows:

[0114]

[0115]

[0116] Where γ is H ∞ Performance index; G j is the intermediate gain matrix; I is the identity matrix with appropriate dimension;

[0117] The steps 3-4 are shown below:

[0118] K j (ε)=G j Z -1 (ε);

[0119] Among them, the superscript symbol -1 represents the inverse operation;

[0120] The steps 3-5 are shown below:

[0121]

[0122] Based on the vector control structure of the motor, the effectiveness and advantages of the proposed TS fuzzy non-cascade servo control strategy are verified, and the interrupt execution cycle is taken as T s =0.1ms, the inverter switching frequency is 10kHz.

[0123] The present invention provides a system for motor modeling and non-cascade servo control method based on TS fuzzy, comprising the following modules:

[0124] The state vector synthesis module is used to select the d-axis and q-axis current signals and angular velocity fed back from the two-phase synchronous rotating orthogonal coordinate system as state variables. In addition, based on the deviation between the angular velocity given signal and the angular velocity, it integrates the deviation to obtain a new state variable, which together constitutes a state vector and is sent to the non-cascaded servo controller module.

[0125] The non-cascaded servo controller module is used to calculate the control vector based on the state vector, the controller membership function and the controller gain matrix that depends on the singular perturbation parameters. The control variables are the voltage control signals u of the d-axis and q-axis respectively. d and u q , send it to the TS fuzzy singular perturbation system module;

[0126] The system disturbance synthesis module is composed of the angular velocity given signal and the load torque disturbance, and is used as the system disturbance input to the TS fuzzy singular perturbation system module;

[0127] The TS fuzzy equation module is used to calculate the response values ​​of the angular velocity and the current signals of the d-axis and q-axis based on the input voltage control signals of the d-axis and q-axis of the two-phase synchronous rotating orthogonal coordinate system, system interference, singular perturbation matrix, model membership function, system matrix, control matrix, and disturbance matrix, and feed them back to the state vector synthesis module as the output signal of the control system.

[0128] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0129] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A motor modeling and non-cascade servo control method based on TS fuzzy, characterized in that: The following steps are involved: Step 1: Establish a mathematical model in a two-phase synchronous rotating orthogonal coordinate system to obtain a state space description; Step 2: According to the state space description, a TS fuzzy model and a closed-loop system are obtained; Step 3: For the closed-loop system, based on Lyapunov stability theory, determine the gain of the non-cascade servo controller.

2. The motor modeling and non-cascade servo control method based on TS fuzzy according to claim 1 is characterized in that: The step 1 specifically includes the following steps: Step 1-1: For the dynamic model of the motor in a three-phase stationary symmetric coordinate system, introduce Clarke transform and Park transform to reduce the order and decouple it. Then, through coordinate transformation, obtain the mathematical model of the motor in a two-phase synchronous rotating orthogonal dq coordinate system. Step 1-2: Introduce state variables to achieve tracking control objectives; Steps 1-3, define the output equation; Steps 1-4: Define singular perturbation parameters; Step 1-5: Combine steps 1-1 to 1-4 to obtain a state space description of the motor singular perturbation model.

3. The motor modeling and non-cascade servo control method based on TS fuzzy according to claim 2 is characterized in that: The step 1 is specifically shown in the following formula: Step 1-1 consists of the electrical dynamics and mechanical equations: Among them, L s and R s are the inductance and resistance of the stator winding respectively; d / dt is the differential operator; i d and i q are the currents of the d-axis and q-axis stator windings in the two-phase synchronous rotating orthogonal dq coordinate system respectively; ψ f is the permanent magnet flux; u d and u q are the voltages of the d-axis and q-axis stator windings respectively; ω e is the electrical angular velocity; Correspondingly, the mechanical dynamic equation is: Where J is the moment of inertia; K T =3p n ψ f / 2 is the electromagnetic torque coefficient, p n is the pole pair number; F is the viscous friction coefficient; T m is the load torque disturbance; ω m is the mechanical angular velocity, and satisfies ω e =p n ω m ; Based on ω e =p n ω m , and the following formula is obtained: The steps 1-2 are shown below: Where e is the tracking error variable; is the first-order differential of the tracking error variable; is the given signal of angular velocity; The steps 1-3 are shown below: y=ω e ; Among them, y is the output variable; The steps 1-4 are shown below: Among them, ε is the singular perturbation parameter; The steps 1-5 are shown below: in, is the singular perturbation model of the motor; E(ε) is the singular perturbation matrix; x=[ω e ei d i q ] T is the state vector, and the superscript symbol T represents transpose; is the first-order differential of the state vector; A is the nonlinear system matrix; B is the control matrix; u=[u d u q ] T is the control vector; D is the perturbation matrix; is the interference; C = [1 0 0 0] is the output matrix.

4. The motor modeling and non-cascade servo control method based on TS fuzzy according to claim 3 is characterized in that: The definitions of the correlation matrices in steps 1-5 are as follows:

5. The motor modeling and non-cascade servo control method based on TS fuzzy according to claim 4 is characterized in that: The step 2 specifically includes the following steps: Step 2-1: To characterize the nonlinear characteristics of the system matrix A, the electrical angular velocity ω e as antecedent variables; Step 2-2: defining the standardized membership functions of two fuzzy rules for the antecedent variables; Step 2-3: Based on the antecedent variables, the nonlinear motor singular perturbation model is equivalent to a TS fuzzy model using the IF-THEN rule; Step 2-4, using the standardized membership function and the TS fuzzy model to obtain a defuzzified model; Step 2-5: Design a defuzzified non-cascade servo controller based on the parallel distributed compensation technology; Step 2-6: Following the fuzzy rules, the defuzzified model and the defuzzified non-cascade servo controller are combined to obtain a closed-loop system.

6. The motor modeling and non-cascade servo control method based on TS fuzzy according to claim 5, characterized in that: The step 2 is shown in the following formula: The prerequisite variable v of step 2-1 is: v=ω e ∈[w W]; Where w and W are the electrical angular velocity ω e the minimum and maximum values ​​of The standardized membership function of step 2-2 is: Among them, μ1=μ1(v) and μ2=μ2(v) are the standardized membership functions corresponding to the two fuzzy rules, and always satisfy the constraint relationship: μ1+μ2=1; The TS fuzzy model in steps 2-3 is composed of several linear subsystems: Among them, i is the rule variable of the model; M i is the fuzzy set corresponding to the fuzzy rule; A i is the linear system matrix corresponding to the i-th rule; r is the number of fuzzy rules, r = 2; The defuzzification model of steps 2-4 is shown below: Among them, μ i is the membership function of the model; Non-cascaded servo controller for steps 2-5: Among them, j is the rule variable of the controller; K j (ε) is the controller gain matrix to be determined; Defuzzified non-cascaded servo controller: Among them, μ j is the membership function of the controller; The closed loop system of steps 2-6:

7. The motor modeling and non-cascade servo control method based on TS fuzzy according to claim 6 is characterized in that: The step 3 specifically includes the following steps: Step 3-1: Given a scalar Set the intermediate variable matrix Z k , k=1, 2, …, 5; and let it satisfy the linear matrix inequality; Step 3-2: For any Determining a relationship between the singular perturbation matrix and the variable matrix dependent on ε; Step 3-3, given scalar γ>0 and Let the intermediate variable matrix Z k And the intermediate gain matrix G j , j=1,2; satisfying the linear matrix inequality; Step 3-4: For any Determine the controller gain matrix; Step 3-5: Determine through the above steps that the closed-loop system is exponentially stable and can satisfy H under zero initial conditions. ∞ performance.

8. The motor modeling and non-cascade servo control method based on TS fuzzy according to claim 7, characterized in that: The step 3 is specifically shown in the following formula: The step 3-1 is shown in the following formula: Z1＞0； in, is the upper bound of the singular perturbation parameter ε; Z k is the intermediate variable matrix and satisfies The symbol * represents a symmetric matrix block of the inducement; 0 represents a zero matrix with appropriate dimensions; The step 3-2 is shown in the following formula: E(e)Z(e)=Z T (e)E(e)>0; The Z(ε) is a variable matrix that depends on ε, and its definition is as follows: The linear matrix inequality satisfied by step 3-3 is as follows: Where γ is H ∞ Performance index; G j is the intermediate gain matrix; I is the identity matrix with appropriate dimension; The steps 3-4 are shown below: K j (e)=G j Z -1 (e); Among them, the superscript symbol -1 represents the inverse operation; The steps 3-5 are shown below:

9. The motor modeling and non-cascade servo control method based on TS fuzzy according to claim 8, characterized in that: Based on the vector control structure of the motor, the effectiveness and advantages of the proposed TS fuzzy non-cascade servo control strategy are verified, and the interrupt execution cycle is taken as T s =0.1ms, the inverter switching frequency is 10kHz.

10. The system of the motor modeling and non-cascade servo control method based on TS fuzzy according to claims 1-9, characterized in that: Includes the following modules: The state vector synthesis module is used to select the d-axis and q-axis current signals and angular velocity fed back from the two-phase synchronous rotating orthogonal coordinate system as state variables. In addition, based on the deviation between the angular velocity given signal and the angular velocity, it integrates the deviation to obtain a new state variable, which together constitutes a state vector and is sent to the non-cascaded servo controller module. a non-cascaded servo controller module, configured to calculate a control vector according to a state vector, a controller membership function, and a controller gain matrix dependent on singular perturbation parameters, and send the control vector to a TS fuzzy singular perturbation system module; The system disturbance synthesis module is composed of the angular velocity given signal and the load torque disturbance, and is used as the system disturbance input to the TS fuzzy singular perturbation system module; The TS fuzzy equation module is used to calculate the response values ​​of the angular velocity and the current signals of the d-axis and q-axis based on the input voltage control signals of the d-axis and q-axis of the two-phase synchronous rotating orthogonal coordinate system, system interference, singular perturbation matrix, model membership function, system matrix, control matrix, and disturbance matrix, and feed them back to the state vector synthesis module as the output signal of the control system.

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