Finite-time quantized control method and system for permanent magnet synchronous motor under multi-scenario coupling

Abstract

The present invention discloses a finite-time quantitative control method and system for a permanent magnet synchronous motor under multi-scenario coupling, wherein the method comprises: providing a permanent magnet synchronous motor model with quantized input; performing coordinate transformation; designing an adaptive law to estimate the optimal approximation weight of a fuzzy logic system; introducing an adaptive dynamic gain; designing an adaptive law for parameters related to stator resistance in the permanent magnet synchronous motor model; iteratively designing a controller according to the tracking error model of the permanent magnet synchronous motor, the adaptive law for the approximation weight of the fuzzy logic system, the adaptive dynamic gain, and the adaptive law for parameters related to stator resistance. The present invention introduces dynamic gain in the adaptive law to realize finite-time arbitrary precision control, thereby avoiding the construction of complex performance functions and coordinate transformation, and solving the potential peaking phenomenon. The input signal is quantized, and the tangent function is introduced to realize implicit controller decoupling, making network control easier to realize.

Description

Technical Field

[0001] The present invention relates to the field of quantized control of permanent magnet synchronous motors, and in particular to a finite time quantized control method and system for a permanent magnet synchronous motor under multi-scenario coupling. Background Art

[0002] In recent years, with the development of power electronics technology and permanent magnet materials, permanent magnet synchronous motors have been widely used in new energy vehicles, precision positioning systems, aerospace and other fields. Permanent magnet synchronous motors have the characteristics of high reliability, high power density, excellent speed regulation performance, energy saving and environmental protection. However, they also have complex nonlinear characteristics and are easily affected by uncertain external factors such as changes in motor working conditions.

[0003] On the other hand, with the rapid development of modern information technology, the application of network control is becoming more and more extensive. Quantization control is an important part of network control. The quantizer converts the continuous signal into a discrete signal with a finite value range, overcoming the influence of communication restrictions on control performance. At present, the quantization control problem of nonlinear permanent magnet synchronous motor with unknown stator resistance in a network environment is one of the research hotspots.

[0004] The backstepping design method can effectively complete the task of stable control of the motor angular velocity. However, in the backstepping iterative design, solving the virtual control signal requires the system to have a strict feedback structure. The permanent magnet synchronous motor itself is a non-strict feedback system under uncertain working conditions, and the actual value of the stator resistance will deviate from the nominal value, making it difficult to solve the virtual control signal. At the same time, in the actual operation of the system, it is often required to achieve higher precision control of the system state or tracking error, that is, to reach the equilibrium point or any specified area within a limited time.

[0005] The current effective method is to pre-introduce performance functions or construct barrier Lyapunov functions to effectively ensure that the system is stable within a finite time and that the tracking error does not violate the pre-specified performance constraints. However, the existing preset performance control requires complex coordinate transformation or complex Lyapunov function construction, and the system trajectory will cause a huge amount of control when it reaches the preset boundary, and may even cause unacceptable peaking. Therefore, it is necessary to propose a new control technology to achieve finite-time arbitrary-precision control of the rotor angle of the permanent magnet synchronous motor under multi-scenario coupling without introducing complex coordinate transformation and avoiding peaking. Summary of the invention

[0006] The present invention aims to solve one of the technical problems existing in the related art at least to a certain extent.

[0007] An object of the present invention is to provide a dynamic gain adaptive quantization control method for a permanent magnet synchronous motor system coupled with multiple scenarios such as unknown stator resistance, nonlinear disturbance and limited communication resources, so as to achieve high-precision tracking of the motor angular position within a limited time.

[0008] Another object of the present invention is to provide a system for applying a finite-time quantized control method of a permanent magnet synchronous motor under multi-scenario coupling.

[0009] In order to achieve the above-mentioned object, the present invention provides, on one hand, a finite-time quantized control method of a permanent magnet synchronous motor under multi-scenario coupling, comprising:

[0010] S100, providing a permanent magnet synchronous motor model with quantized input;

[0011] S200, coordinate transformation is performed;

[0012] S300, designing an adaptive law to estimate the optimal approximation weight of the fuzzy logic system;

[0013] S400, introduces adaptive dynamic gain;

[0014] S500, design the adaptive law of parameters related to stator resistance in the permanent magnet synchronous motor model;

[0015] S600, iteratively designing a controller according to a tracking error model of a permanent magnet synchronous motor, an adaptive law of an approximation weight of a fuzzy logic system, an adaptive dynamic gain, and an adaptive law of parameters related to stator resistance.

[0016] A further preferred technical solution of the present invention is that step S100 provides a permanent magnet synchronous motor model with quantized input, including establishing a state equation describing the dynamic behavior of the permanent magnet synchronous motor in a synchronous rotating dq coordinate system, as a permanent magnet synchronous motor model, including rotor angle, angular velocity, d-axis and q-axis current components and corresponding voltage components.

[0017] Preferably, in step S100, a hysteresis quantizer with a specific quantization density and dead zone is designed, and the control signal of the permanent magnet synchronous motor is quantized by the hysteresis quantizer.

[0018] Preferably, the coordinate transformation described in step S200 is used to simplify the design process of the controller, and the specific method is to transform the original motor state variables by introducing new variables.

[0019] Preferably, the step S300 of designing an adaptive law to estimate the optimal approximation weights of the fuzzy logic system includes designing an adaptive law to estimate unknown weights in the fuzzy logic system.

[0020] Preferably, an adaptive dynamic gain is introduced in step S400 to achieve finite-time arbitrary-precision control and avoid constructing complex performance functions and coordinate transformations;

[0021] In step S500 , an adaptive law is designed to estimate uncertain resistance parameters in the permanent magnet synchronous motor model.

[0022] The adaptive dynamic gain achieves finite-time arbitrary-precision control by introducing dynamic gain variables. This control strategy avoids constructing complex performance functions and coordinate transformations in the design, as well as possible peaking phenomena near preset boundaries.

[0023] Preferably, the controller is iteratively designed according to the tracking error model of the permanent magnet synchronous motor, the adaptive law of the fuzzy logic system approximation weight, the adaptive dynamic gain, and the adaptive law of the parameters related to the stator resistance in step S600, and the specific method is:

[0024] S610, decomposing the permanent magnet synchronous motor model into multiple subsystems, and analyzing each subsystem according to different dynamic characteristics of the motor;

[0025] S620, constructing a candidate Lyapunov function for each subsystem;

[0026] S630, using a fuzzy logic system to approximate the nonlinear part in the subsystem, wherein the fuzzy logic system includes a set of IF-THEN rules for processing nonlinear characteristics in the motor model;

[0027] S640. Based on the approximation results of the candidate Lyapunov function and the fuzzy logic system, design the virtual control function of the feedback control law to achieve the stability and performance goals in each subsystem;

[0028] S650, according to the stability and performance requirements of the subsystem, the control input considering the quantization error and communication limitation is designed. The control input is a voltage command used to directly control the dynamic behavior of the motor;

[0029] S660, the control input is continuously optimized through an iterative process to adapt to changes in motor parameters and external disturbances. The iterative optimization process uses historical data and current errors to adjust the control strategy.

[0030] On the other hand, the present invention provides a finite-time quantized control system of a permanent magnet synchronous motor under multi-scenario coupling. The system adopts the control method as described above to realize the control of the permanent magnet synchronous motor under multi-scenario coupling.

[0031] Another aspect of the present invention provides a non-transitory computer-readable storage medium having computer instructions stored thereon, the computer instructions enabling a computer to execute the above control method.

[0032] Another aspect of the present invention provides an electronic device, comprising: a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus, and the processor calls the logic instructions in the memory to execute the above-mentioned control method.

[0033] Another aspect of the present invention provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer executes the above-mentioned control method.

[0034] Beneficial effects: The dynamic gain finite-time quantized control method and system of the permanent magnet synchronous motor system of the present invention takes into account the permanent magnet synchronous motor system with multi-scenario coupling, that is, the system has the structural characteristics of non-strict feedback, the stator resistance with unknown true value and the quantized input in the network environment, and solves the problem of finite-time arbitrary precision control by systematically designing an adaptive controller. A fuzzy logic system is used to approximate the non-strict feedback nonlinear part in the permanent magnet synchronous motor, so that the virtual control signal can be solved, and an adaptive law is constructed in combination with adaptive technology to compensate for the unknown resistance in the motor system and the unknown weight in the fuzzy logic system. Different from the traditional preset performance control and barrier Lyapunov function method, dynamic gain is introduced in the adaptive law to realize finite-time arbitrary precision control, thereby avoiding the construction of complex performance functions and coordinate transformations, and solving the potential peaking phenomenon. The input signal is quantized, and the tangent function is introduced to realize implicit controller decoupling, making network control easier to implement. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 is a control structure block diagram of the control method of the present invention;

[0036] Figure 2 A comparison diagram of angle tracking response results between the method of the present invention and the non-finite time quantization control method;

[0037] Figure 3 This is a comparison chart of the angle tracking error results between the method of the present invention and the non-finite time quantization control method. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings in the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments, and they should not be understood as limitations on the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. In the description of the present invention, it should be understood that the terms used are only for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0039] Combine the following Figures 1 to 3 The present invention describes a finite-time quantitative control method and system for a permanent magnet synchronous motor under multi-scenario coupling.

[0040] Embodiment 1: This embodiment provides a finite time quantized control method for a permanent magnet synchronous motor under multi-scenario coupling. In terms of overall design, Figure 1 As shown, including:

[0041] S100, providing a permanent magnet synchronous motor model with quantized input;

[0042] S200, coordinate transformation is performed;

[0043] S300, designing an adaptive law to estimate the optimal approximation weight of the fuzzy logic system;

[0044] S400, introduces adaptive dynamic gain;

[0045] S500, design the adaptive law of parameters related to stator resistance in the permanent magnet synchronous motor model;

[0046] S600, iteratively designing a controller according to a tracking error model of a permanent magnet synchronous motor, an adaptive law of an approximation weight of a fuzzy logic system, an adaptive dynamic gain, and an adaptive law of parameters related to stator resistance.

[0047] The specific steps are:

[0048] (1) Step S100 provides a permanent magnet synchronous motor model with quantized input, including establishing a state equation describing the dynamic behavior of the permanent magnet synchronous motor in a synchronous rotating dq coordinate system as a permanent magnet synchronous motor model, including a rotor angle, an angular velocity, a d-axis and a q-axis current component, and a corresponding voltage component. At the same time, a hysteresis quantizer with a specific quantization density and dead zone is designed, and the control signal of the permanent magnet synchronous motor is quantized by the hysteresis quantizer.

[0049] In the synchronous rotating dq coordinate system, the state equation of the permanent magnet synchronous motor system is expressed as:

[0050]

[0051] In the formula, x1 = θ, x2 = ω, x3 = i q , x4=i d , d1=cos(t). θ, ω are the rotor angle and rotor angular velocity of the motor; i d 、i q are the current components of the d and q axes in the synchronously rotating dq coordinate system; u d 、u q are the voltage components of the d and q axes respectively; q(u q )、q(u d ) represent the voltage quantization values ​​of the d and q axes respectively. d , L q are the inductances of the d and q axes respectively; J is the equivalent moment of inertia; T L is the load torque of the motor; M is the viscous friction coefficient; R s is the stator resistance of the motor; n p is the number of stator winding poles; Φ is the rotor flux, h1 is the nonlinear function part, and d1 is the external interference.

[0052] The signal is quantized using the following hysteresis quantizer:

[0053]

[0054] In the formula, i =1,2,…, ,and , , . , express The previous state. Parameters Determines the dead zone of the quantizer near zero, parameter and Decided together Quantized density.

[0055] For the hysteresis quantizer in the above formula, there is the following lemma:

[0056] Lemma 1: For the hysteresis quantizer in equation (2), in order to limit the quantization error, the following properties apply:

[0057] |q(u)-u|≤δ|u|+d#(3)

[0058] In the above formula, 0<δ<1, d>0.

[0059] (2) The coordinate transformation in S200 is specifically as follows:

[0060] Introduce the following coordinate transformation:

[0061]

[0062] In the formula, y d To track the target signal, α1 and α2 are virtual control signals.

[0063] (3) The design of the adaptive law in S300 estimates the optimal approximation weight of the fuzzy logic system, specifically:

[0064] Design an adaptive law to estimate the optimal approximation weight of the fuzzy logic system, and its structural expression is:

[0065]

[0066] In the formula, are the estimated values ​​of Θ1 and Θ2 respectively. Θ1 and Θ2 are the unknown weights in the fuzzy logic system. k1, k2>0 are adjustable constant gains. λ is the adaptive dynamic gain.

[0067] (4) The introduction of the adaptive dynamic gain in S400 is specifically:

[0068] In formula (5), λ is the adaptive dynamic gain. By introducing the dynamic gain variable λ, finite-time arbitrary precision control is achieved, avoiding the construction of complex performance functions and coordinate transformations in the design, as well as the peaking phenomenon that may exist near the preset boundary. Its expression is as follows:

[0069]

[0070] (5) The adaptive law of the parameters related to the stator resistance in the design of the permanent magnet synchronous motor model described in S500 is specifically:

[0071] Design the adaptive law of the parameters γ1 and γ2 related to the stator resistance in the permanent magnet synchronous motor model. The adaptive law expression of γ1 is:

[0072]

[0073] Where σ>0 is the design parameter. The adaptive law expression of γ2 is:

[0074]

[0075] (6) S600 iteratively designs the controller according to the tracking error model of the permanent magnet synchronous motor, the adaptive law of the fuzzy logic system approximation weight, the adaptive dynamic gain, and the adaptive law of the parameters related to the stator resistance, specifically:

[0076] In the first step, consider the e1 subsystem and construct the candidate Lyapunov function as follows:

[0077]

[0078] In the formula, To estimate the error, take the derivative of the above formula and combine e2=x2-α1 in formula (4) with formula (5) to obtain

[0079]

[0080] For the above equation, a fuzzy logic system is used to approximate the nonlinear part h1(x1). The IF-THEN rule used by the fuzzy logic system is:

[0081] Rule j IFx1is andx2is and…and

[0082] x r is THEN z is j ,j=1,2,…,N

[0083] The fuzzy logic system is constructed according to N rules using a product inference engine, a single-value fuzzifier and a central average defuzzifier:

[0084]

[0085] In the formula, x=(x1,x2,…,x r ), μF(·) and μG(·) are the membership functions of fuzzy sets F and G respectively. is the fuzzy set G j The center of is realized by using a single-value fuzzifier. The fuzzy basis function is:

[0086]

[0087] For the above fuzzy logic system, there is the following lemma:

[0088] Lemma 2 For a continuous function f(x) defined on a compact set Ω, there exists a fuzzy logic system (6) satisfying

[0089] f(x)=Θ T ξ(x)+Δ#(13)

[0090] Where Δ>0 is an arbitrary constant and satisfies |Δ|<Δ * , Δ * Is a normal number.

[0091] make Available

[0092]

[0093] Assume that the external disturbance d1 is bounded and has an upper bound Make And assume that the tracking target signal y d is 2nd order differentiable, is piecewise continuous and bounded. Combined with Lemma 2, we can get:

[0094]

[0095] Design the virtual control function as

[0096]

[0097] Where m1>0 is the design parameter. and Combining the above formula with formula (15), we can get

[0098]

[0099] In the second step, consider the (e1, e2) subsystem and construct the candidate Lyapunov function as follows:

[0100]

[0101] In the formula, is the estimation error. Taking the derivative of equation (18), combined with e3=x3-α2 in equation (4), we can get

[0102]

[0103] make Using fuzzy logic system approximation The nonlinear part in In order to more accurately perform backstepping iterative design of the system, the fuzzy logic system is transformed into Separation Combining formula (5), we can get

[0104]

[0105] because

[0106]

[0107] In addition, the calculation can be obtained

[0108]

[0109] Design the virtual control function as

[0110]

[0111] In the formula, m2>0 is the design parameter. and Combining equation (17), equation (19), equation (20), equation (21), and equation (22), we can get

[0112]

[0113] The third step is to consider the (e1, e2, e3) subsystem. Since the size of the resistor will change due to environmental factors during the actual operation of the system, the actual value of the resistor is unknown and different from the nominal value. γ1 in equation (2) is estimated online. The candidate Lyapunov function is constructed as follows:

[0114]

[0115] In the formula, β1>0 is the design parameter, is the estimated value of the unknown parameter γ1 in the system. Taking the derivative of the above equation, we can get

[0116]

[0117] In order to realize the implicit decoupling of the quantitative controller, the tangent function is introduced and u is designed. q In the following form:

[0118]

[0119] In the formula, u′ q is the quantity to be designed. Multiply both sides of equation (3) by |e3|, and after calculation and combining with the above equation, we can get the following inequality:

[0120]

[0121] In order to deal with quantization errors, this paper adopts the following hyperbolic tangent function properties. For any ε>0, There are the following inequalities:

[0122]

[0123] Transforming equation (28) and using equation (29), we can obtain the following inequality:

[0124]

[0125] In the above formula, η≥d. Notice Substituting equation (30) into equation (26), we can obtain

[0126]

[0127] According to equation (27) and equation (31), the control input u is designed q as follows

[0128]

[0129] In the formula, m3>0 is the design parameter. Combining formula (7), formula (24) and formula (31), we can get

[0130]

[0131] The fourth step is to consider the entire (e1, e2, e3, e4) system and construct the final Lyapunov function:

[0132]

[0133] In the formula, β2>0 is the design parameter, is the estimated value of the unknown parameter γ2 in the system. By taking the derivative of the above formula, we can get

[0134]

[0135] Similar to equations (27) and (30), combined with equation (35), we can get

[0136]

[0137] Design control input u d as follows

[0138]

[0139] In the formula, m4>0 is the design parameter, and u′ in formula (37) is d Substituting into formula (36), combining formula (8) and formula (33), we can get

[0140]

[0141] (7) Stability analysis:

[0142] definition

[0143]

[0144] Finally available

[0145]

[0146] Theorem 1: For system (1), the design control signals are as shown in equations (32) and (37), the virtual control signals are as shown in equations (16) and (23), and the adaptive control laws are as shown in equations (5), (6), (7) and (8), then the signal of the closed-loop system is bounded, and there exists a finite time T such that |x1-y d |≤ζ.

[0147] Proof According to formula (6), Combining formula (39), we can get

[0148]

[0149] Multiply both sides of the above formula by Available

[0150]

[0151] Note that M0>0, Integrating equation (41) on [0, t] yields

[0152]

[0153] According to formula (34), we have Combining formula (42), we can get

[0154]

[0155] That is e i is bounded, and similarly Θ1, Θ2, γ1, and γ2 are bounded.

[0156] The boundedness of λ is proved by contradiction. Assuming λ is not bounded, then there exists a finite time T1 such that

[0157]

[0158] Substituting equation (44) into equation (39) yields

[0159]

[0160] Integrating equation (45) on [0, t] yields

[0161]

[0162] Notice Existence for a limited time Make

[0163]

[0164] Let T = max{T1, T2}, and from equation (46) and equation (47) we get

[0165]

[0166] Combining formula (39), we can get Further available

[0167] |e1|≤ζ,t≥T#(49)

[0168] According to formula (4) and formula (6), we can get

[0169]

[0170] This shows that λ is bounded, which contradicts the assumption. It is further proved that there is a finite time T that makes the tracking error |e1| shrink to a given error accuracy ζ.

[0171] In order to verify the finite time quantized control method of the permanent magnet synchronous motor under multi-scenario coupling of this embodiment, a simulation experiment is carried out and compared with the traditional non-finite time quantized control method. The comparison results are as follows: Figure 2 and Figure 3 As shown, it can be seen that the finite time quantization control catches up with the reference signal in a shorter time and has better tracking performance.

[0172] The relevant parameters of the permanent magnet synchronous motor used in the simulation are shown in Table 1.

[0173] Table 1 Permanent magnet synchronous motor parameter values

[0174]

[0175] The control law parameters are selected as k1=0.4, k2=0.4, m1=100, m2=100, m3=100, m4=100, σ=1, β1=1, β2=1, η=0.1. When k2=0, the finite time control method degenerates into a non-finite time control method. The quantizer parameters are selected as d=0.1, δ=0.25. The tracking target signal is y d =sin2t, tracking accuracy ζ=0.05.

[0176] Embodiment 2: This embodiment provides a finite-time quantized control system for a permanent magnet synchronous motor under multi-scenario coupling. The system adopts the control method of embodiment 1 to achieve high-precision angular position tracking even under the conditions of multi-scenario coupling and limited communication resources.

[0177] Embodiment 3: This embodiment provides a non-transitory computer-readable storage medium on which computer instructions are stored. The computer instructions enable a computer to execute the control method of embodiment 1.

[0178] Embodiment 4: This embodiment provides an electronic device, which may include: a processor, a communications interface, a memory, and a communication bus, wherein the processor, the communications interface, and the memory communicate with each other via the communication bus. The processor may call the logic instructions in the memory to execute the control method of embodiment 1.

[0179] In addition, the logic instructions in the above-mentioned memory can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0180] Embodiment 5: This embodiment provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the control method of Embodiment 1.

[0181] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0182] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0183] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A finite time quantitative control method for a permanent magnet synchronous motor under multi-scenario coupling, characterized in that: include: S100, providing a permanent magnet synchronous motor model with quantized input; S200, coordinate transformation is performed; it is expressed as: In the formula, y d is to track the target signal, α1 and α2 are virtual control signals, x1 = θ, x2 = ω, x3 = i q , x4=i d , θ, ω are the rotor angle and rotor angular velocity of the motor, i d 、i q are the current components of the d and q axes in the synchronously rotating dq coordinate system, respectively; S300, design an adaptive law to estimate the optimal approximation weight of the fuzzy logic system; its structural expression is: In the formula, are the estimated values ​​of Θ1 and Θ2 respectively. Θ1 and Θ2 are the unknown weights in the fuzzy logic system. k1, k2>0 are adjustable constant gains. λ is the adaptive dynamic gain; S400, introduces adaptive dynamic gain; Its expression is as follows: Where ζ is the error accuracy; S500, design the adaptive law of parameters related to stator resistance in the permanent magnet synchronous motor model; S600, iteratively designing a controller according to a tracking error model of a permanent magnet synchronous motor, an adaptive law of an approximation weight of a fuzzy logic system, an adaptive dynamic gain, and an adaptive law of parameters related to stator resistance.

2. The finite time quantization control method of a permanent magnet synchronous motor under multi-scenario coupling according to claim 1 is characterized in that: Step S100 provides a permanent magnet synchronous motor model with quantized input, including establishing a state equation describing the dynamic behavior of the permanent magnet synchronous motor in a synchronous rotating dq coordinate system, as a permanent magnet synchronous motor model, including rotor angle, angular velocity, d-axis and q-axis current components and corresponding voltage components.

3. The finite time quantized control method of a permanent magnet synchronous motor under multi-scenario coupling according to claim 2 is characterized in that: In step S100, a hysteresis quantizer with a specific quantization density and dead zone is designed, and the control signal of the permanent magnet synchronous motor is quantized by the hysteresis quantizer.

4. The finite time quantized control method of a permanent magnet synchronous motor under multi-scenario coupling according to claim 1 is characterized in that: Step S600 iteratively designs the controller according to the tracking error model of the permanent magnet synchronous motor, the adaptive law of the fuzzy logic system approximation weight, the adaptive dynamic gain, and the adaptive law of the parameters related to the stator resistance. The specific method is: S610, decomposing the permanent magnet synchronous motor model into multiple subsystems, and analyzing each subsystem according to different dynamic characteristics of the motor; S620, constructing a candidate Lyapunov function for each subsystem; S630, using a fuzzy logic system to approximate the nonlinear part in the subsystem, wherein the fuzzy logic system includes a set of IF-THEN rules for processing nonlinear characteristics in the motor model; S640. Based on the approximation results of the candidate Lyapunov function and the fuzzy logic system, design the virtual control function of the feedback control law to achieve the stability and performance goals in each subsystem; S650, according to the stability and performance requirements of the subsystem, the control input considering the quantization error and communication limitation is designed. The control input is a voltage command used to directly control the dynamic behavior of the motor; S660, the control input is continuously optimized through an iterative process to adapt to changes in motor parameters and external disturbances. The iterative optimization process uses historical data and current errors to adjust the control strategy.

5. A finite time quantitative control system of a permanent magnet synchronous motor under multi-scenario coupling, characterized in that: The system adopts the control method as described in any one of claims 1 to 4 to realize the control of the permanent magnet synchronous motor under multi-scenario coupling.

6. A non-transitory computer-readable storage medium having computer instructions stored thereon, the computer instructions enabling a computer to execute the control method according to any one of claims 1 to 4.

7. An electronic device comprising: A processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus, and the processor calls the logic instructions in the memory to execute the control method described in any one of claims 1 to 4.

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