Motor model prediction torque control method and system based on weight coefficient adjustment
By adaptively adjusting the weighting coefficients of the motor model predictive torque control using fuzzy logic rules, the problem of poor control performance caused by fixed weighting coefficients in traditional methods is solved, thus improving the all-condition performance of motor control, especially torque tracking and flux linkage optimization during dynamic processes.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-28
- Publication Date
- 2026-03-10
AI Technical Summary
In traditional motor model predictive torque control, the weighting coefficients are fixed, resulting in poor control performance when the electromagnetic torque reference value or stator flux reference value changes, making it difficult to maintain excellent control performance across the entire operating range.
The weighting coefficients are automatically adjusted based on the motor's operating status using fuzzy logic rules. By obtaining the speed error and its absolute value, the error is mapped to the fuzzy quantity of the model's predictive torque control cost function and then defuzzified to adaptively adjust the weighting coefficients.
It achieves adaptive adjustment of weight coefficients under different motor operating conditions, improving the system control performance of model predictive torque control. In particular, it can quickly track torque and optimize flux linkage waveform during dynamic processes, reduce torque ripple, and has strong robustness and ease of implementation.
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Figure CN121643558A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application belongs to the technical field of motor control, and particularly relates to a motor model predictive torque control method and system based on weight coefficient adjustment. BACKGROUND
[0002] The statements in this section merely provide background information related to the present application and do not necessarily constitute the prior art.
[0003] Model predictive torque control (MPTC), which is a control strategy combining model predictive control (MPC) and direct torque control (DTC), is mainly used for high-performance control of permanent magnet synchronous motors (PMSM) to achieve accurate control of torque and flux by optimizing voltage vector selection. Based on the motor mathematical model (such as Euler's formula), the torque and flux values at the next moment are predicted, and the current state (current, angle, etc.) is calculated; the effects of different voltage vectors are evaluated through a cost function (such as the weighted sum of torque error and flux error), and the vector that minimizes the cost function is selected as the optimal control quantity; the rolling optimization process of prediction-evaluation-selection is repeated in each cycle to dynamically adjust the control strategy.
[0004] Model predictive torque control has fast dynamic response, easy handling of multiple target constraints, and has been widely used in the field of permanent magnet synchronous motor control; its core lies in selecting the optimal voltage vector through optimizing the cost function. In traditional motor model predictive control, the value of the cost function is calculated at each control cycle by designing the cost function, and the voltage vector that minimizes the cost function is selected as the input of the permanent magnet synchronous motor model predictive control algorithm in the next control cycle, wherein the first term of the cost function corresponds to the control target electromagnetic torque; the second term of the cost function corresponds to the control target stator flux; however, the setting of the first weight coefficient and the second weight coefficient based on empirical values does not change throughout the control process, which leads to poor control effect of traditional induction motor in model predictive control when the electromagnetic torque reference value or the stator flux reference value changes; that is, by designing two weight coefficients to adjust the relative importance of the control target torque and the stator flux, and then affecting the overall control effect.
[0005] Therefore, how to realize the adaptive adjustment of the weight coefficient to improve the control performance of the model predictive torque control in the whole operating range is a difficult problem to be solved. SUMMARY
[0006] To solve the above problems, the present application proposes a motor model predictive torque control method and system based on weight coefficient adjustment, which automatically adjusts the weight coefficient according to the motor operating state to optimize the control performance under different motor operating conditions.
[0007] According to some embodiments, the first aspect of the present invention provides a motor model predictive torque control method based on weight coefficient adjustment, employing the following technical solution: A motor model predictive torque control method based on weighted coefficient adjustment includes: Obtain the actual speed of the motor; The motor's speed error and absolute value are calculated based on the obtained actual speed. Based on the obtained speed error and absolute value of the error, determine the current operating status of the motor; Based on the current operating state of the motor and fuzzy logic rules, the fuzzy quantities of speed error and absolute error value are mapped to the fuzzy quantities of weight coefficients in the model predictive torque control cost function; The fuzzy values of the mapped weight coefficients are defuzzified to obtain the weight coefficient values. The model predictive torque control cost function is adaptively adjusted based on the obtained weight coefficient values to achieve motor model predictive torque control based on weight coefficient adjustment.
[0008] As a further technical limitation, the current operating state of the motor includes at least the starting state, the overspeed adjustment state, the deceleration adjustment state, and the static stable state.
[0009] As a further technical limitation, the membership function is a fuzzy subset, that is, the fuzzy subset of the rotational speed error is {NB, NM, NS, ZE, PS, PM, PB}; the fuzzy subset of the absolute value of the error is {NB, PB}; and the fuzzy subset of the weighting coefficient is {NB, NM, NS, ZE, PS, PM, PB}.
[0010] Furthermore, the fuzzy logic rule is as follows: When the absolute value of the error is PB, the value of the weighting coefficient is the same as the value of the rotational speed error; When the absolute value of the error is NB, the value of the weighting coefficient shifts negatively relative to the value of the rotational speed error.
[0011] Furthermore, the shift in the negative direction includes: If the speed error is PM, then the weighting coefficient is PS; If the speed error is NM, then the weighting coefficient is NS.
[0012] As a further technical limitation, the fuzzy values of the mapped weight coefficients are defuzzified based on the fuzzy rule table and the defuzzification language rules.
[0013] According to some embodiments, a second aspect of the present invention provides a motor model predictive torque control system based on weighted coefficient adjustment, employing the following technical solution: A motor model predictive torque control system based on weighted coefficient adjustment, comprising: The acquisition module is configured to acquire the actual speed of the motor; The calculation module is configured to calculate the motor's speed error and absolute value of the error based on the acquired actual speed. The judgment module is configured to determine the current operating status of the motor based on the obtained speed error and absolute value of the error; The mapping module is configured to map the fuzzy quantities of speed error and absolute error value to the fuzzy quantities of weight coefficients in the model predictive torque control cost function based on the current operating state of the motor and fuzzy logic rules. The processing module is configured to defuzzify the fuzzy values of the mapped weight coefficients to obtain the weight coefficient values. The adjustment module is configured to adaptively adjust the model predictive torque control cost function based on the obtained weight coefficient values, thereby realizing motor model predictive torque control based on weight coefficient adjustment.
[0014] According to some embodiments, a third aspect of the present invention provides a computer-readable storage medium, employing the following technical solution: A computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps in the motor model predictive torque control method based on weight coefficient adjustment as described in the first aspect of the present invention.
[0015] According to some embodiments, the fourth aspect of the present invention provides an electronic device, which adopts the following technical solution: An electronic device includes a memory, a processor, and a program stored in the memory and running on the processor, wherein the processor executes the program to implement the steps in the motor model predictive torque control method based on weight coefficient adjustment as described in the first aspect of the present invention.
[0016] According to some embodiments, the fifth aspect of the present invention provides a computer program product, which adopts the following technical solution: A computer program product includes software code, wherein the program in the software code performs the steps of the motor model predictive torque control method based on weight coefficient adjustment as described in the first aspect of the present invention.
[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention uses fuzzy logic to associate weighting coefficients with speed errors and their changing trends that reflect system dynamics. This allows the weighting coefficients to automatically adjust according to different motor operating states (such as dynamic acceleration, deceleration, and steady state) without the need for repeated manual tuning. During dynamic processes, adjusting the weighting coefficients prioritizes ensuring rapid torque tracking. During steady-state processes, adjusting the weighting coefficients optimizes the flux linkage waveform and reduces torque ripple, thereby improving the system's control performance across the entire operating range. Fuzzy logic itself is insensitive to parameter changes and nonlinear factors, making this adaptive method highly robust. The logic is clear, requires no additional complex hardware, and is easily implemented on existing digital control platforms. Attached Figure Description
[0018] The accompanying drawings, which form part of this embodiment, are used to provide a further understanding of this embodiment. The illustrative embodiments and their descriptions are used to explain this embodiment and do not constitute an improper limitation of this embodiment.
[0019] Figure 1 This is a flowchart of the motor model predictive torque control method based on weight coefficient adjustment in Embodiment 1 of the present invention; Figure 2 This is a block diagram of the motor model predictive control structure in Embodiment 1 of the present invention; Figure 3 This is a classification diagram for judging the motor operating status in Embodiment 1 of the present invention; Figure 4 This is a schematic diagram of the membership function of rotational speed error in Embodiment 1 of the present invention; Figure 5 This is a schematic diagram of the membership function of the rotational speed error hysteresis width in Embodiment 1 of the present invention; Figure 6 This is a schematic diagram of the membership function of the weight coefficients in Embodiment 1 of the present invention; Figure 7 This is a flowchart illustrating the influence of motor operating status on weighting coefficients in Embodiment 1 of the present invention; Figure 8 This is a block diagram of the motor model predictive torque control system based on weight coefficient adjustment in Embodiment 2 of the present invention. Detailed Implementation
[0020] The present invention will be further described below with reference to the accompanying drawings and embodiments.
[0021] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.
[0022] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.
[0023] In this invention, terms such as "upper," "lower," "left," "right," "front," "back," "vertical," "horizontal," "side," and "bottom" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used only to facilitate the description of the structural relationships of the various components or elements of this invention and do not specifically refer to any component or element in this invention. They should not be construed as limiting the invention.
[0024] In this invention, terms such as "fixed connection," "connected," and "linked" should be interpreted broadly, indicating a fixed connection, an integral connection, or a detachable connection; a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can determine the specific meaning of these terms in this invention based on the specific circumstances, and they should not be construed as limitations on the invention.
[0025] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.
[0026] Example 1 Embodiment 1 of this invention introduces a motor model predictive torque control method based on weight coefficient adjustment.
[0027] like Figure 1 The method for predictive torque control of a motor model based on weighted coefficient adjustment, as shown, includes: Obtain the actual speed of the motor; The motor's speed error and absolute value are calculated based on the obtained actual speed. Based on the obtained speed error and absolute value of the error, determine the current operating status of the motor; Based on the current operating state of the motor and fuzzy logic rules, the fuzzy quantities of speed error and absolute error value are mapped to the fuzzy quantities of weight coefficients in the model predictive torque control cost function; The fuzzy values of the mapped weight coefficients are defuzzified to obtain the weight coefficient values. The model predictive torque control cost function is adaptively adjusted based on the obtained weight coefficient values to achieve motor model predictive torque control based on weight coefficient adjustment.
[0028] This embodiment uses speed error to determine several operating states of the motor. Then, based on the error and the absolute value of the error relative to the fixed speed error, and combined with fuzzy logic rules, the relationship between speed error and weight coefficients is established. By defuzzification, the weight coefficients of torque and flux linkage are adjusted, thereby achieving adaptive adjustment of the weight coefficients of the cost function in model predictive torque control.
[0029] In practical applications, the value of the weighting coefficient is crucial to system performance. If the weighting coefficient is too large, it overemphasizes flux control, leading to sluggish torque response; if the weighting coefficient is too small, the flux control effect deteriorates, potentially causing increased torque ripple or even system instability. Currently, the weighting coefficient is usually a fixed constant, requiring extensive simulations and repeated experimental adjustments, which is time-consuming and labor-intensive. Furthermore, a fixed weighting coefficient value is unlikely to maintain optimal performance under all motor operating conditions; for example, the emphasis on torque and flux differs between dynamic and steady-state processes. Therefore, how to achieve adaptive adjustment of the weighting coefficient to improve the control performance of MPTC across the entire operating range has become a pressing technical problem in this field.
[0030] like Figure 2 The model predictive torque control (MPTC) shown can be used to obtain the motor's... The equation for the shaft stator voltage is: (1) in, , , , , and These represent the stator voltage, current, and inductance in the rotor reference frame, respectively. shaft and Axial components; , and These represent the stator resistance, permanent magnet flux linkage, and electric angular rotational speed, respectively.
[0031] Discretize the obtained formula (1) to obtain the current prediction equation, i.e. (2) in, , They represent the kth and (k+1)th times, respectively. Shaft sampling current, , They represent the kth and (k+1)th times, respectively. Shaft sampling current, This is the interruption cycle.
[0032] like Figure 2 The electromagnetic torque equation of the motor, derived from the model predictive torque control (MPTC) shown, is as follows: (3) in, Indicates electromagnetic torque. Indicates the number of pole pairs of the motor, , They represent shaft and Component of magnetic flux linkage in axial permanent magnet.
[0033] Surface-mounted permanent magnet synchronous motor =0, the electromagnetic torque formula is: (4) Electromagnetic torque prediction equation: (5) in, This represents the value of the electromagnetic torque at (K+1) times.
[0034] The equation for the axial flux linkage is: (6) The axis flux linkage prediction equation is: (7) in, express The value of the (K+1)th order magnetic flux linkage component of the axial permanent magnet. express The value of the flux linkage component (K+1) of the axial permanent magnet.
[0035] The stator flux linkage equation is: (8) The stator flux linkage prediction equation is: (9) in, Indicates stator flux linkage The value at (K+1)th time.
[0036] (10) in, Indicates the stator flux reference value, This represents the value of the stator flux linkage at order (K+1). Indicates the reference value of electromagnetic torque. This represents the value of the electromagnetic torque at the (K+1)th time. This represents the weighting coefficient.
[0037] In this embodiment, the value function formula (10) first calculates the magnetic flux reference value. The value of the stator flux linkage (K+1)th order absolute value of the difference , and then with For electromagnetic torque reference value and The absolute value of the difference between the (K+1)th values of the electromagnetic torque. Multiply by weighting factor The result is obtained by addition; where, This represents the weighting coefficient. Because the cost function designed for model predictive torque control has different dimensions for torque and flux linkage, weighting coefficients are needed. This is to achieve weighted adjustment of torque and flux linkage.
[0038] It should be noted that, without considering the demagnetization effect, in this embodiment... It can be set to a constant value. In practical applications, repeated adjustments are needed based on simulation and experimental results to obtain the optimal control effect.
[0039] like Figure 7 As shown, this embodiment determines the operating state of several motors by calculating the motor speed error. Then, based on the error and the absolute value of the error relative to the fixed speed error, fuzzy logic rules are used to establish the speed error and weighting coefficients. The relationship between torque and flux linkage is adjusted by defuzzification. To realize the weighting coefficients of the cost function in model predictive torque control. The adaptive adjustment completes the motor model predictive torque control.
[0040] As one or more implementation methods, this embodiment detects the motor speed. Based on the detected rotational speed Calculate the motor speed error and absolute value of error (In this embodiment, the absolute value of the error) Not exceeding the speed error threshold ,Right now It should be noted that calculating the speed error and the absolute value of the error based on the speed is prior art that should be known to those skilled in the art, and will not be described in detail here.
[0041] This embodiment is based on speed error. and absolute value of error The system determines the motor's motion state after four types of speed changes: starting state, overspeed adjustment, deceleration adjustment, and static stability. Specifically: like Figure 3 As shown, based on the rotational speed and speed error, the rotational speed gradually approaches the reference speed from the start, which is judged as starting state 1; after the rotational speed exceeds the reference speed, it gradually decreases to the reference speed, which is judged as overspeed and adjustment 2; after the rotational speed decreases to below the reference speed, it gradually rises to the reference speed, which is judged as deceleration and adjustment 3; the speed error meets the requirements. It is judged to be statically stable 4.
[0042] This embodiment is based on speed error. and absolute value of error Determine the membership functions for each type; specifically: Based on the speed error, the range of speed error is divided into 7 segments: {-500, -200, -100, 0, 100, 200, 500}, and the membership function of speed error is set accordingly. ;like Figure 4 and Figure 5 As shown, the membership function of the speed error is {NB, NM, NS, ZE, PS, PM, PB}, where -500 is NB, -200 is NM, -100 is NS, 0 is ZE, 100 is PS, 200 is PM, and 500 is PB; the membership function of the absolute value of the speed error is {NB, PB}, where {error less than 0, error greater than 0}, and the corresponding error less than 0 is NB, and the error greater than 0 is PB. If , then .
[0043] like Figure 6 As shown, the weighting coefficients in this embodiment The membership function is {NB,NM,NS,ZE,PS,PM,PB}{-20,-10,-2,0,2,10,20}; corresponding to -20 for NB, -10 for NM, -2 for NS, 0 for ZE, 2 for PS, 10 for PM, and 20 for PB.
[0044] This embodiment uses the fuzzy logic rule table shown in Table 1 to defuzzify the fuzzy values of the mapped weight coefficients, for example... For PM, For PM, For PM, through its membership function, The value is set to 20; this will be the adaptively adjusted weighting coefficient. Substituting numerical values into the weighting function The adaptive adjustment of the weight function is realized to complete the motor model predictive torque control based on the adjustment of the weight coefficient.
[0045] Table 1 Fuzzy Logic Rule Table
[0046] This embodiment uses fuzzy logic to associate weighting coefficients with the speed error and its changing trend, which reflects the dynamics of the system. This allows the weighting coefficients to be automatically adjusted according to different operating states of the motor (such as dynamic acceleration, deceleration, and steady state) without the need for repeated manual tuning. During dynamic processes, adjusting the weighting coefficients prioritizes ensuring rapid torque tracking. During steady-state processes, adjusting the weighting coefficients optimizes the flux linkage waveform and reduces torque ripple, thereby improving the system's control performance across the entire operating range. Fuzzy logic itself is insensitive to parameter changes and nonlinear factors, making this adaptive method highly robust. The logic is clear, requires no additional complex hardware, and is easy to implement on existing digital control platforms.
[0047] Example 2 Embodiment 2 of the present invention introduces a motor model predictive torque control system based on weight coefficient adjustment.
[0048] like Figure 8 The motor model predictive torque control system shown includes: The acquisition module is configured to acquire the actual speed of the motor; The calculation module is configured to calculate the motor's speed error and absolute value of the error based on the acquired actual speed. The judgment module is configured to determine the current operating status of the motor based on the obtained speed error and absolute value of the error; The mapping module is configured to map the fuzzy quantities of speed error and absolute error value to the fuzzy quantities of weight coefficients in the model predictive torque control cost function based on the current operating state of the motor and fuzzy logic rules. The processing module is configured to defuzzify the fuzzy values of the mapped weight coefficients to obtain the weight coefficient values. The adjustment module is configured to adaptively adjust the model predictive torque control cost function based on the obtained weight coefficient values, thereby realizing motor model predictive torque control based on weight coefficient adjustment.
[0049] The detailed steps are the same as those of the motor model predictive torque control method based on weight coefficient adjustment provided in Example 1, and will not be repeated here.
[0050] Example 3 Embodiment 3 of the present invention provides a computer-readable storage medium.
[0051] A computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps in the motor model predictive torque control method based on weight coefficient adjustment as described in Embodiment 1 of the present invention.
[0052] The detailed steps are the same as those of the motor model predictive torque control method based on weight coefficient adjustment provided in Example 1, and will not be repeated here.
[0053] Example 4 Embodiment 4 of the present invention provides an electronic device.
[0054] An electronic device includes a memory, a processor, and a program stored in the memory and running on the processor. When the processor executes the program, it implements the steps in the motor model predictive torque control method based on weight coefficient adjustment as described in Embodiment 1 of the present invention.
[0055] The detailed steps are the same as those of the motor model predictive torque control method based on weight coefficient adjustment provided in Example 1, and will not be repeated here.
[0056] Example 5 Embodiment 5 of the present invention provides a computer program product.
[0057] A computer program product includes software code, wherein the program in the software code performs the steps of the motor model predictive torque control method based on weight coefficient adjustment as described in Embodiment 1 of the present invention.
[0058] The detailed steps are the same as those of the motor model predictive torque control method based on weight coefficient adjustment provided in Example 1, and will not be repeated here.
[0059] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.
[0060] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0061] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0062] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0063] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.
[0064] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.
[0065] The above description is merely a preferred embodiment of this practice and is not intended to limit the scope of this practice. Various modifications and variations can be made to this practice by those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of this practice should be included within the protection scope of this practice.
Claims
1. A motor model predictive torque control method based on weight coefficient adjustment, characterized in that, The method comprises: acquiring an actual rotating speed of the motor; calculating a rotating speed error and an error absolute value of the motor based on the acquired actual rotating speed; judging a current operating state of the motor according to the obtained rotating speed error and error absolute value; mapping fuzzy quantities of the rotating speed error and error absolute value to fuzzy quantities of a weight coefficient in a model predictive torque control cost function based on the current operating state of the motor and fuzzy logic rules; de-fuzzifying the mapped fuzzy quantities of the weight coefficient to obtain a weight coefficient value; adjusting the model predictive torque control cost function according to the obtained weight coefficient value to realize motor model predictive torque control based on weight coefficient adjustment.
2. A model predictive torque control method for electric machines based on weight coefficient adjustment as claimed in claim 1, characterized in that, The current operating state of the motor at least includes a starting state, an overspeed adjustment state, a deceleration adjustment state and a static stable state.
3. A model predictive torque control method for electric machines based on weight coefficient adjustment as claimed in claim 1, characterized in that, The membership function is a fuzzy subset, i.e., the fuzzy subset of the rotating speed error is {NB, NM, NS, ZE, PS, PM, PB}; the fuzzy subset of the error absolute value is {NB, PB}; and the fuzzy subset of the weight coefficient is {NB, NM, NS, ZE, PS, PM, PB}.
4. A model predictive torque control method for electric machines based on weight coefficient adjustment as claimed in claim 3, characterized in that, The fuzzy logic rules are: when the error absolute value is PB, the value of the weight coefficient is the same as the value of the rotating speed error; when the error absolute value is NB, the value of the weight coefficient is offset to the negative direction relative to the value of the rotating speed error.
5. A model predictive torque control method for electric machines based on weight coefficient adjustment as claimed in claim 4, characterized in that, The offset to the negative direction includes: if the rotating speed error is PM, the weight coefficient takes PS; if the rotating speed error is NM, the weight coefficient takes NS.
6. A model predictive torque control method for electric machines based on weight coefficient adjustment as claimed in claim 1, characterized by, The de-fuzzifying is based on a fuzzy rule table and de-fuzzifying language rules.
7. A motor model predictive torque control system based on weight coefficient adjustment, characterized in that, The method comprises: an acquiring module configured to acquire an actual rotating speed of the motor; a calculating module configured to calculate a rotating speed error and an error absolute value of the motor based on the acquired actual rotating speed; a judging module configured to judge a current operating state of the motor according to the obtained rotating speed error and error absolute value; a mapping module configured to map fuzzy quantities of the rotating speed error and error absolute value to fuzzy quantities of a weight coefficient in a model predictive torque control cost function based on the current operating state of the motor and fuzzy logic rules; a processing module configured to de-fuzzify the mapped fuzzy quantities of the weight coefficient to obtain a weight coefficient value; an adjusting module configured to adjust the model predictive torque control cost function according to the obtained weight coefficient value to realize motor model predictive torque control based on weight coefficient adjustment.
8. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to realize the steps of the motor model predictive torque control method based on weight coefficient adjustment as claimed in any one of claims 1-6.
9. An electronic device comprising a memory, a processor, and a computer program stored on the memory and running on the processor, characterized in that, The processor executes the program to realize the steps of the motor model predictive torque control method based on weight coefficient adjustment as claimed in any one of claims 1-6.
10. A computer program product comprising software code, characterized in that, The program in the software code executes the steps of the motor model predictive torque control method based on weight coefficient adjustment as claimed in any one of claims 1-6.