A method and system for optimizing motor control performance

Abstract

The present invention discloses a method and system for optimizing motor control performance, belonging to the field of motor control technology. The method of the present invention comprises: after a high-frequency signal is injected into a target motor, voltage compensation is performed on the target motor under the condition of the high-frequency signal being injected; based on the high-frequency signal injected into the target motor, the rotor position of the target motor is determined, and based on the rotor position, closed-loop control is performed on the target motor through a control system; and the control performance of the target motor is optimized through the voltage compensation and closed-loop control. The present invention can be used for optimizing the control performance of a motor.

Description

Technical Field

[0001] The present invention relates to the field of motor control technology, and more particularly, to a method and system for optimizing motor control performance. Background Art

[0002] The motor body with the characteristic of suppressing high-frequency harmonics has certain characteristics in terms of armature winding arrangement, stator and rotor tooth profiles, etc. How to understand and design the motor body that meets the control requirements is a key technical problem that needs to be solved. Summary of the invention

[0003] In view of the above problems, the present invention proposes a method for optimizing motor control performance, comprising:

[0004] After the high-frequency signal is injected into the target motor, voltage compensation is performed on the target motor under the condition of the high-frequency signal being injected;

[0005] Based on the high-frequency signal injected into the target motor, the rotor position of the target motor is determined, and based on the rotor position, the target motor is closed-loop controlled through a control system;

[0006] The control performance of the target motor is optimized through the voltage compensation and closed-loop control.

[0007] Optionally, voltage compensation is performed on the target motor when the high frequency signal is injected, including:

[0008] Acquiring electrical parameters of the target motor, establishing a discrete prediction model of the target motor based on the electrical parameters, and determining the current polarity of the target motor in a zero current region according to the discrete prediction model;

[0009] In the case of injecting a high-frequency signal into a target motor, determining influencing factors affecting a voltage error of the target motor, establishing a voltage error model of the target motor based on the influencing factors, and determining a voltage error curve of the target motor in the case of injecting a high-frequency signal according to the voltage error model;

[0010] Based on the current polarity of the target motor in the zero current region and the voltage error curve when a high-frequency signal is injected, the voltage value that needs to be compensated for the target motor when a high-frequency signal is injected is determined, and based on the voltage value, voltage compensation is performed on the target motor when a high-frequency signal is injected.

[0011] Optionally, establishing a discrete prediction model of the target motor based on the electrical parameters includes:

[0012] Normalizing the electrical parameters, using the normalized electrical parameters as input data of the single-layer neural network, and using the predicted value of the target motor current as output data of the single-layer neural network;

[0013] The input data and the output data are simultaneously input into a single-layer neural network and trained using a gradient descent method to obtain a discrete prediction model of the target motor.

[0014] Optionally, the established discrete prediction model is fitted based on the adaptability of the neural network to improve the robustness of the discrete prediction model.

[0015] Optionally, the influencing factors include at least one of the following: dead time, conduction judgment time, switch tube conduction voltage drop and parasitic capacitance.

[0016] Optionally, the method further includes: decoupling the mid-base frequency part and the high frequency part of the voltage error model to determine a high frequency component expression of the voltage error, and based on the high frequency component expression, correcting the voltage error curve determined by the voltage error model.

[0017] Optionally, closed-loop control of the target motor is performed, including:

[0018] After the high-frequency signal is injected into the target motor, the fault feature quantity of the target motor is extracted by a preset convex optimization extraction scheme;

[0019] Based on the extracted fault feature quantity, and taking the speed and rotor position estimated by the monitoring prediction algorithm of the target motor as independent variables, an objective function is established. If the objective function is a convex function, the independent variables are input into the objective function as input values, so as to prompt the objective function to perform solution calculation and obtain a real-time objective function value of the objective function;

[0020] When a target function value in any time unit exceeds a preset threshold, determining a rotor position estimated by a monitoring prediction algorithm of the target motor at this time, and performing a rotor position correction on a control system of the target motor based on the estimated rotor position;

[0021] After the control system of the target motor is modified, the phase angle of the two-phase current envelope of the target motor is obtained by using a preset envelope extraction scheme, and the phase angle is filtered;

[0022] The rotor position is determined based on the filtered phase angle, which enables the control system to perform closed-loop control on the target motor based on the rotor position to improve the stability of the target motor operation.

[0023] Optionally, whether the objective function is a convex function is determined based on eigenvalues ​​of a Hessian matrix of the objective function.

[0024] Optionally, an estimation error of the estimation algorithm is monitored to set a threshold, and the threshold is modified according to the time-varying characteristics of the target motor control system parameters.

[0025] Optionally, obtaining a real-time objective function value of the objective function includes: calculating the operation status of the target motor monitoring estimation algorithm in real time through the objective function to obtain the real-time objective function value.

[0026] Optionally, the estimation accuracy of the monitoring estimation algorithm is compensated by eliminating the effects of lag and cross saturation of current sampling in the target motor on the position estimation.

[0027] Optionally, a preset envelope extraction scheme is established based on the peak hold circuit principle of the target motor.

[0028] Optionally, a preset envelope extraction scheme is used to obtain the phase angle of the two-phase current envelope of the target motor, including: using the preset envelope extraction scheme to extract the low-frequency component associated with the motor speed in the high-frequency current response of the target motor, determining the two-phase current envelope of the target motor based on the low-frequency component, normalizing the two-phase current envelope, determining the sine and cosine values ​​of the two-phase current envelope, and calculating the sine and cosine values ​​using an inverse tangent function to obtain the phase angle of the two-phase current envelope of the target motor.

[0029] Optionally, the phase angle is filtered by a phase-locked loop observer.

[0030] In another aspect, the present invention further provides a system for optimizing motor control performance, comprising:

[0031] A first optimization module is used for performing voltage compensation on the target motor when the high-frequency signal is injected into the target motor;

[0032] A second optimization module is used to determine the rotor position of the target motor based on the high-frequency signal injected by the target motor, and perform closed-loop control on the target motor through a control system based on the rotor position;

[0033] The third optimization module is used to optimize the control performance of the target motor through the voltage compensation and closed-loop control.

[0034] Optionally, voltage compensation is performed on the target motor when the high frequency signal is injected, including:

[0035] Acquiring electrical parameters of the target motor, establishing a discrete prediction model of the target motor based on the electrical parameters, and determining the current polarity of the target motor in a zero current region according to the discrete prediction model;

[0036] In the case of injecting a high-frequency signal into a target motor, determining influencing factors affecting a voltage error of the target motor, establishing a voltage error model of the target motor based on the influencing factors, and determining a voltage error curve of the target motor in the case of injecting a high-frequency signal according to the voltage error model;

[0037] Based on the current polarity of the target motor in the zero current region and the voltage error curve when a high-frequency signal is injected, the voltage value that needs to be compensated for the target motor when a high-frequency signal is injected is determined, and based on the voltage value, voltage compensation is performed on the target motor when a high-frequency signal is injected.

[0038] Optionally, establishing a discrete prediction model of the target motor based on the electrical parameters includes:

[0039] Normalizing the electrical parameters, using the normalized electrical parameters as input data of the single-layer neural network, and using the predicted value of the target motor current as output data of the single-layer neural network;

[0040] The input data and the output data are simultaneously input into a single-layer neural network and trained using a gradient descent method to obtain a discrete prediction model of the target motor.

[0041] Optionally, the established discrete prediction model is fitted based on the adaptability of the neural network to improve the robustness of the discrete prediction model.

[0042] Optionally, the influencing factors include at least one of the following: dead time, conduction judgment time, switch tube conduction voltage drop and parasitic capacitance.

[0043] Optionally, the method further includes: decoupling the mid-base frequency part and the high frequency part of the voltage error model to determine a high frequency component expression of the voltage error, and based on the high frequency component expression, correcting the voltage error curve determined by the voltage error model.

[0044] Optionally, closed-loop control of the target motor is performed, including:

[0045] After the high-frequency signal is injected into the target motor, the fault feature quantity of the target motor is extracted by a preset convex optimization extraction scheme;

[0046] Based on the extracted fault feature quantity, and taking the speed and rotor position estimated by the monitoring prediction algorithm of the target motor as independent variables, an objective function is established. If the objective function is a convex function, the independent variables are input into the objective function as input values, so as to prompt the objective function to perform solution calculation and obtain a real-time objective function value of the objective function;

[0047] When a target function value in any time unit exceeds a preset threshold, determining a rotor position estimated by a monitoring prediction algorithm of the target motor at this time, and performing a rotor position correction on a control system of the target motor based on the estimated rotor position;

[0048] After the control system of the target motor is modified, the phase angle of the two-phase current envelope of the target motor is obtained by using a preset envelope extraction scheme, and the phase angle is filtered;

[0049] The rotor position is determined based on the filtered phase angle, which enables the control system to perform closed-loop control on the target motor based on the rotor position to improve the stability of the target motor operation.

[0050] Optionally, the eigenvalues ​​of the Hessian matrix of the objective function determine whether the objective function is convex.

[0051] Optionally, an estimation error of the estimation algorithm is monitored to set a threshold, and the threshold is modified according to the time-varying characteristics of the target motor control system parameters.

[0052] Optionally, obtaining a real-time objective function value of the objective function includes: calculating the operation status of the target motor monitoring estimation algorithm in real time through the objective function to obtain the real-time objective function value.

[0053] Optionally, the estimation accuracy of the monitoring estimation algorithm is compensated after eliminating the influence of hysteresis and cross saturation of current sampling in the target motor on the position estimation.

[0054] Optionally, a preset envelope extraction scheme is established based on the peak hold circuit principle of the target motor.

[0055] Optionally, a preset envelope extraction scheme is used to obtain the phase angle of the two-phase current envelope of the target motor, including: using the preset envelope extraction scheme to extract the low-frequency component associated with the motor speed in the high-frequency current response of the target motor, determining the two-phase current envelope of the target motor based on the low-frequency component, normalizing the two-phase current envelope, determining the sine and cosine values ​​of the two-phase current envelope, and calculating the sine and cosine values ​​using an inverse tangent function to obtain the phase angle of the two-phase current envelope of the target motor.

[0056] Optionally, the phase angle is filtered by a phase-locked loop observer.

[0057] In yet another aspect, the present invention further provides a computing device, comprising: one or more processors;

[0058] a processor for executing one or more programs;

[0059] When the one or more programs are executed by the one or more processors, the above-described method is implemented.

[0060] In yet another aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed, the method described above is implemented.

[0061] Compared with the prior art, the present invention has the following beneficial effects:

[0062] The method of the present invention comprises: after a high-frequency signal is injected into a target motor, voltage compensation is performed on the target motor in the case of the injection of the high-frequency signal; based on the high-frequency signal injected into the target motor, the rotor position of the target motor is determined, and based on the rotor position, closed-loop control is performed on the target motor through a control system; and the control performance of the target motor is optimized through the voltage compensation and closed-loop control. The present invention can be used for optimizing the control performance of a motor. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 is a flow chart of the method of the present invention;

[0064] Figure 2 Flow chart of the system of the present invention. DETAILED DESCRIPTION

[0065] Now, exemplary embodiments of the present invention are described with reference to the accompanying drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. These embodiments are provided to disclose the present invention in detail and completely and to fully convey the scope of the present invention to those skilled in the art. The terms used in the exemplary embodiments shown in the accompanying drawings are not intended to limit the present invention. In the accompanying drawings, the same units / elements are marked with the same reference numerals.

[0066] Unless otherwise specified, the terms (including technical terms) used herein have the commonly understood meanings to those skilled in the art. In addition, it is understood that the terms defined in commonly used dictionaries should be understood to have the same meanings as those in the context of the relevant fields, and should not be understood as idealized or overly formal meanings.

[0067] Embodiment 1:

[0068] The present invention proposes a method for optimizing motor control performance, such as Figure 1 As shown, including:

[0069] Step 1, after the high-frequency signal is injected into the target motor, voltage compensation is performed on the target motor under the condition of the high-frequency signal being injected;

[0070] Step 2: determining the rotor position of the target motor based on the high-frequency signal injected by the target motor, and performing closed-loop control on the target motor through a control system based on the rotor position;

[0071] Step 3: Optimize the control performance of the target motor through the voltage compensation and closed-loop control.

[0072] The voltage compensation of the target motor when the high-frequency signal is injected includes:

[0073] Acquiring electrical parameters of the target motor, establishing a discrete prediction model of the target motor based on the electrical parameters, and determining the current polarity of the target motor in a zero current region according to the discrete prediction model;

[0074] In the case of injecting a high-frequency signal into a target motor, determining influencing factors affecting a voltage error of the target motor, establishing a voltage error model of the target motor based on the influencing factors, and determining a voltage error curve of the target motor in the case of injecting a high-frequency signal according to the voltage error model;

[0075] Based on the current polarity of the target motor in the zero current region and the voltage error curve when a high-frequency signal is injected, the voltage value that needs to be compensated for the target motor when a high-frequency signal is injected is determined, and based on the voltage value, voltage compensation is performed on the target motor when a high-frequency signal is injected.

[0076] Wherein, establishing a discrete prediction model of the target motor based on the electrical parameters includes:

[0077] Normalizing the electrical parameters, using the normalized electrical parameters as input data of the single-layer neural network, and using the predicted value of the target motor current as output data of the single-layer neural network;

[0078] The input data and the output data are simultaneously input into a single-layer neural network and trained using a gradient descent method to obtain a discrete prediction model of the target motor.

[0079] Among them, the established discrete prediction model is fitted based on the adaptability of the neural network to improve the robustness of the discrete prediction model.

[0080] The influencing factors include at least one of the following: dead time, conduction judgment time, switch tube conduction voltage drop and parasitic capacitance.

[0081] The method further includes: decoupling the mid-base frequency part and the high frequency part of the voltage error model to determine the high frequency component expression of the voltage error, and based on the high frequency component expression, correcting the voltage error curve determined by the voltage error model.

[0082] Among them, closed-loop control of the target motor includes:

[0083] After the high-frequency signal is injected into the target motor, the fault feature quantity of the target motor is extracted by a preset convex optimization extraction scheme;

[0084] Based on the extracted fault feature quantity, and taking the speed and rotor position estimated by the monitoring prediction algorithm of the target motor as independent variables, an objective function is established. If the objective function is a convex function, the independent variables are input into the objective function as input values, so as to prompt the objective function to perform solution calculation and obtain a real-time objective function value of the objective function;

[0085] When a target function value in any time unit exceeds a preset threshold, determining a rotor position estimated by a monitoring prediction algorithm of the target motor at this time, and performing a rotor position correction on a control system of the target motor based on the estimated rotor position;

[0086] After the control system of the target motor is modified, the phase angle of the two-phase current envelope of the target motor is obtained by using a preset envelope extraction scheme, and the phase angle is filtered;

[0087] The rotor position is determined based on the filtered phase angle, which enables the control system to perform closed-loop control on the target motor based on the rotor position to improve the stability of the target motor operation.

[0088] Wherein, whether the objective function is a convex function is determined based on the Hessian matrix eigenvalue of the objective function.

[0089] Wherein, a threshold is set based on an estimation error of a monitoring estimation algorithm, and the threshold is corrected according to a time-varying characteristic of a target motor control system parameter.

[0090] Wherein, obtaining the real-time objective function value of the objective function includes: calculating the operation status of the target motor monitoring estimation algorithm in real time through the objective function to obtain the real-time objective function value.

[0091] Among them, the estimation accuracy of the monitoring estimation algorithm is compensated by eliminating the influence of the lag and cross saturation of the current sampling in the target motor on the position estimation.

[0092] Among them, the preset envelope extraction scheme is established according to the peak holding circuit principle of the target motor.

[0093] Among them, a preset envelope extraction scheme is used to obtain the phase angle of the two-phase current envelope of the target motor, including: using the preset envelope extraction scheme to extract the low-frequency component associated with the motor speed in the high-frequency current response of the target motor, determining the two-phase current envelope of the target motor based on the low-frequency component, normalizing the two-phase current envelope, determining the sine and cosine values ​​of the two-phase current envelope, and calculating the sine and cosine values ​​through the inverse tangent function to obtain the phase angle of the two-phase current envelope of the target motor.

[0094] The phase angle is filtered by a phase-locked loop observer.

[0095] The principles of the embodiments of the present invention are as follows:

[0096] The stability problem of the traditional salient pole tracking solution is reflected in the dynamic process of the motor's rapid acceleration or deceleration or torque disturbance. It is possible that the position observation converges to the opposite of the actual position due to large transient errors. From the perspective of fault diagnosis, the position estimation can be detected in real time, and fault tolerance processing can be quickly performed after a fault occurs to restore the system to normal steady-state operation.

[0097] When designing the scheme, it is planned to use the convex optimization theory in optimal control to extract the fault characteristics: the objective function is designed with the estimated speed and position as independent variables, and the Hessian matrix eigenvalues ​​are analyzed to determine that the objective function is a convex function, and the global minimum value appears when the estimated value is equal to the true value. The operation of the estimation algorithm is monitored by real-time calculation of the objective function. When the objective function value exceeds the set threshold, it is considered that the position estimation has converged incorrectly, and the position estimation is corrected to make the system return to a stable state.

[0098] The setting of the threshold should fully consider the impact of the estimation error in the dynamic process, and leave an appropriate margin to reduce the false alarm rate. Considering the time-varying characteristics of the parameters of the motor control system, the parameter sensitivity is studied by deriving the electrical parameters of the objective function, and the key parameters are obtained for online identification. In the absence of position sensors, the speed and position variables are also obtained by observation. Therefore, the parameter identification system is a multi-input and multi-output system, and the identification results affect each other. The erroneous convergence caused by the "lack of rank problem" in multi-parameter identification can be avoided by reasonably adding steady-state operating points; the stability of the identification system can also be improved by step-by-step identification of fast and slow time-varying parameters.

[0099] 2) Design a direct calculation scheme for the rotor position. The starting point of this scheme is to extract the low-frequency component related to the motor speed in the high-frequency current response, and directly calculate the phase angle through the inverse tangent function. From existing research results, it can be seen that when the high-frequency voltage is injected into the direct axis in the estimated two-phase rotating coordinate system, the frequency of the high-frequency current envelope after the motor salient polarity modulation in the two-phase stationary coordinate system is related to the motor speed. It is proposed to use the peak holding circuit principle to design an envelope extraction scheme that is conducive to the implementation of digital control systems, normalize the extracted two-phase current envelope to obtain the sine and cosine values, and use the inverse tangent function to calculate the phase angle. Since the direct calculation method is susceptible to noise interference, it is considered to filter the calculated phase angle through a phase-locked loop observer, and finally use it for closed-loop control.

[0100] This scheme avoids the unstable zero point problem existing in the traditional phase-locked scheme. However, since the position information is contained in the low-frequency component of the high-frequency current response, it is necessary to study the phase lag caused by low-pass filtering. At the same time, it is necessary to compensate for the influence of current sampling lag and cross-saturation on position estimation in order to improve the estimation accuracy.

[0101] Among the existing compensation schemes, the compensation scheme based on voltage pulse adjustment is the most widely used and most suitable for the specific application background of position sensor-free. The key points that determine the accuracy of such compensation schemes are: first, the accurate judgment of the current polarity in the zero current region, and second, the accuracy of the voltage error curve.

[0102] Regarding the first point, this project intends to design a current prediction scheme from the perspective of model prediction to solve the delay problem in digital implementation. The current prediction model can be obtained by Euler discretization of the stator current equation, but the model has strong parameter dependence. It is planned to use the adaptability of the neural network to fit the discrete function to improve the parameter robustness of the current prediction. From the perspective of reducing the computational complexity and combining the characteristics of the discrete prediction model, it is planned to select a single-layer neural network structure, treat the electrical parameter items as weight factors that need to be adjusted online, and use the predicted value of the current as the output of the neural network. The difference between the current and the sampled current is combined with the gradient descent method to train the neural network. In the specific implementation, the input items should be normalized to avoid possible numerical problems, and the influence of the learning step size on the convergence speed and prediction accuracy should be studied. In order to further improve the accuracy of current prediction, the current oversampling scheme within a single cycle is studied. The increased current sampling points help to accurately obtain the current change law under the conditions of known switch tube state and action time, thereby improving the function fitting accuracy. Regarding the second point, this project established a voltage error model that includes influencing factors such as dead time, conduction judgment time, switch tube conduction voltage drop, parasitic capacitance, etc., and designed the voltage error curve offline on this basis; studied the same-frequency fluctuation characteristics of the voltage error caused by repeated zero crossing of the high-frequency current in the zero current region when the high-frequency voltage signal was injected; by decoupling the fundamental frequency part and the high-frequency part in the voltage error model, the expression of the high-frequency component in the voltage error was obtained, and based on this, the voltage error curve in the zero current region was corrected.

[0103] Embodiment 2:

[0104] The present invention also proposes a system 200 for optimizing motor control performance, such as Figure 2 As shown, including:

[0105] The first optimization module 201 is used to perform voltage compensation on the target motor after the high-frequency signal is injected into the target motor;

[0106] A second optimization module 202 is used to determine the rotor position of the target motor based on the high-frequency signal injected by the target motor, and perform closed-loop control on the target motor through a control system based on the rotor position;

[0107] The third optimization module 203 is used to optimize the control performance of the target motor through the voltage compensation and closed-loop control.

[0108] The voltage compensation is performed on the target motor when the high-frequency signal is injected, including:

[0109] Acquiring electrical parameters of the target motor, establishing a discrete prediction model of the target motor based on the electrical parameters, and determining the current polarity of the target motor in a zero current region according to the discrete prediction model;

[0110] In the case of injecting a high-frequency signal into a target motor, determining influencing factors affecting a voltage error of the target motor, establishing a voltage error model of the target motor based on the influencing factors, and determining a voltage error curve of the target motor in the case of injecting a high-frequency signal according to the voltage error model;

[0111] Based on the current polarity of the target motor in the zero current region and the voltage error curve when a high-frequency signal is injected, the voltage value that needs to be compensated for the target motor when a high-frequency signal is injected is determined, and based on the voltage value, voltage compensation is performed on the target motor when a high-frequency signal is injected.

[0112] Wherein, establishing a discrete prediction model of the target motor based on the electrical parameters includes:

[0113] Normalizing the electrical parameters, using the normalized electrical parameters as input data of the single-layer neural network, and using the predicted value of the target motor current as output data of the single-layer neural network;

[0114] The input data and the output data are simultaneously input into a single-layer neural network and trained using a gradient descent method to obtain a discrete prediction model of the target motor.

[0115] Among them, the established discrete prediction model is fitted based on the adaptability of the neural network to improve the robustness of the discrete prediction model.

[0116] The influencing factors include at least one of the following: dead time, conduction judgment time, switch tube conduction voltage drop and parasitic capacitance.

[0117] Among them, the first optimization module is also used to: decouple the mid-base frequency part and the high-frequency part of the voltage error model to determine the high-frequency component expression of the voltage error, and based on the high-frequency component expression, correct the voltage error curve determined by the voltage error model.

[0118] Among them, closed-loop control of the target motor includes:

[0119] After the high-frequency signal is injected into the target motor, the fault feature quantity of the target motor is extracted by a preset convex optimization extraction scheme;

[0120] Based on the extracted fault feature quantity, and taking the speed and rotor position estimated by the monitoring prediction algorithm of the target motor as independent variables, an objective function is established. If the objective function is a convex function, the independent variables are input into the objective function as input values, so as to prompt the objective function to perform solution calculation and obtain a real-time objective function value of the objective function;

[0121] When a target function value in any time unit exceeds a preset threshold, determining a rotor position estimated by a monitoring prediction algorithm of the target motor at this time, and performing a rotor position correction on a control system of the target motor based on the estimated rotor position;

[0122] After the control system of the target motor is modified, the phase angle of the two-phase current envelope of the target motor is obtained by using a preset envelope extraction scheme, and the phase angle is filtered;

[0123] The rotor position is determined based on the filtered phase angle, which enables the control system to perform closed-loop control on the target motor based on the rotor position to improve the stability of the target motor operation.

[0124] Wherein, whether the objective function is a convex function is determined based on the Hessian matrix eigenvalue of the objective function.

[0125] Wherein, a threshold is set based on an estimation error of a monitoring estimation algorithm, and the threshold is corrected according to a time-varying characteristic of a target motor control system parameter.

[0126] Wherein, obtaining the real-time objective function value of the objective function includes: calculating the operation status of the target motor monitoring estimation algorithm in real time through the objective function to obtain the real-time objective function value.

[0127] Among them, the estimation accuracy of the monitoring estimation algorithm is compensated by eliminating the influence of the lag and cross saturation of the current sampling in the target motor on the position estimation.

[0128] Among them, the preset envelope extraction scheme is established according to the peak holding circuit principle of the target motor.

[0129] Among them, a preset envelope extraction scheme is used to obtain the phase angle of the two-phase current envelope of the target motor, including: using the preset envelope extraction scheme to extract the low-frequency component associated with the motor speed in the high-frequency current response of the target motor, determining the two-phase current envelope of the target motor based on the low-frequency component, normalizing the two-phase current envelope, determining the sine and cosine values ​​of the two-phase current envelope, and calculating the sine and cosine values ​​through the inverse tangent function to obtain the phase angle of the two-phase current envelope of the target motor.

[0130] The phase angle is filtered by a phase-locked loop observer.

[0131] Embodiment 3:

[0132] Based on the same inventive concept, the present invention also provides a computer device, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding functions, so as to implement the steps of the method in the above embodiment.

[0133] Embodiment 4:

[0134] Based on the same inventive concept, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here can include both a built-in storage medium in a computer device and an extended storage medium supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of the method in the above embodiment.

[0135] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may 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 codes. The schemes in the embodiments of the present invention may be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0136] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0137] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0138] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0139] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0140] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for optimizing motor control performance, characterized in that: The method comprises: After the high-frequency signal is injected into the target motor, voltage compensation is performed on the target motor under the condition of the high-frequency signal being injected; Based on the high-frequency signal injected into the target motor, the rotor position of the target motor is determined, and based on the rotor position, the target motor is closed-loop controlled through a control system; Optimizing the control performance of the target motor through the voltage compensation and closed-loop control; The voltage compensation of the target motor when the high-frequency signal is injected includes: Acquiring electrical parameters of the target motor, establishing a discrete prediction model of the target motor based on the electrical parameters, and determining the current polarity of the target motor in a zero current region according to the discrete prediction model; In the case of injecting a high-frequency signal into a target motor, determining influencing factors affecting a voltage error of the target motor, establishing a voltage error model of the target motor based on the influencing factors, and determining a voltage error curve of the target motor in the case of injecting a high-frequency signal according to the voltage error model; Based on the current polarity of the target motor in the zero current region and the voltage error curve when the high-frequency signal is injected, determine the voltage value of the target motor to be compensated when the high-frequency signal is injected, and perform voltage compensation on the target motor when the high-frequency signal is injected according to the voltage value; A discrete prediction model of the target motor is established based on the electrical parameters, including: Normalizing the electrical parameters, using the normalized electrical parameters as input data of the single-layer neural network, and using the predicted value of the target motor current as output data of the single-layer neural network; The input data and the output data are simultaneously input into a single-layer neural network and trained using a gradient descent method to obtain a discrete prediction model of the target motor; Among them, the established discrete prediction model is fitted based on the adaptability of the neural network to improve the robustness of the discrete prediction model; The influencing factors include at least one of the following: dead time, conduction judgment time, switch tube conduction voltage drop and parasitic capacitance; The mid-base frequency part and the high frequency part of the voltage error model are decoupled to determine the high frequency component expression of the voltage error, and based on the high frequency component expression, the voltage error curve determined by the voltage error model is corrected; The closed-loop control of the target motor includes: After the high-frequency signal is injected into the target motor, the fault feature quantity of the target motor is extracted by a preset convex optimization extraction scheme; Based on the extracted fault feature quantity, and taking the speed and rotor position estimated by the monitoring prediction algorithm of the target motor as independent variables, an objective function is established. If the objective function is a convex function, the independent variables are input into the objective function as input values, so as to prompt the objective function to perform solution calculation and obtain the objective function value of the objective function; When a target function value in any time unit exceeds a preset threshold, determining a rotor position estimated by a monitoring prediction algorithm of the target motor at this time, and performing a rotor position correction on a control system of the target motor based on the estimated rotor position; After the control system of the target motor is modified, the phase angle of the two-phase current envelope of the target motor is obtained by using a preset envelope extraction scheme, and the phase angle is filtered; Determine the rotor position based on the filtered phase angle, and enable the control system to perform closed-loop control on the target motor based on the rotor position to improve the stability of the target motor operation; Wherein, whether the objective function is a convex function is determined based on the Hessian matrix eigenvalue of the objective function; Wherein, a threshold is set based on an estimation error of a monitoring estimation algorithm, and the threshold is corrected according to a time-varying characteristic of a target motor control system parameter; Wherein, obtaining the objective function value of the objective function includes: calculating the operation status of the target motor monitoring estimation algorithm in real time through the objective function to obtain the objective function value; Among them, the estimation accuracy of the monitoring estimation algorithm is compensated after eliminating the influence of the hysteresis and cross saturation of the current sampling in the target motor on the position estimation; Wherein, the preset envelope extraction scheme is established according to the peak holding circuit principle of the target motor; The method of obtaining the phase angle of the two-phase current envelope of the target motor by using a preset envelope extraction scheme includes: extracting the low-frequency component associated with the motor speed in the high-frequency current response of the target motor by using the preset envelope extraction scheme, determining the two-phase current envelope of the target motor for the low-frequency component, normalizing the two-phase current envelope, determining the sine and cosine values ​​of the two-phase current envelope, and calculating the sine and cosine values ​​by an inverse tangent function to obtain the phase angle of the two-phase current envelope of the target motor; Among them, the objective function is designed with the estimated speed and rotor position as independent variables. The Hessian matrix eigenvalue of the objective function is used to determine that when the objective function is a convex function, the global minimum occurs when the estimated value is equal to the true value. The operation status is determined by monitoring the estimation algorithm through real-time calculation of the objective function. When the objective function value exceeds the set threshold, erroneous convergence of the position estimation occurs. The position estimation is corrected to make the target motor re-enter a stable state.

2. A system for optimizing motor control performance, characterized in that: The system comprises: A first optimization module is used for performing voltage compensation on the target motor when the high-frequency signal is injected into the target motor; A second optimization module is used to determine the rotor position of the target motor based on the high-frequency signal injected by the target motor, and perform closed-loop control on the target motor through a control system based on the rotor position; A third optimization module, used for optimizing the control performance of the target motor through the voltage compensation and closed-loop control; The voltage compensation of the target motor when the high-frequency signal is injected includes: Acquiring electrical parameters of the target motor, establishing a discrete prediction model of the target motor based on the electrical parameters, and determining the current polarity of the target motor in a zero current region according to the discrete prediction model; In the case of injecting a high-frequency signal into a target motor, determining influencing factors affecting a voltage error of the target motor, establishing a voltage error model of the target motor based on the influencing factors, and determining a voltage error curve of the target motor in the case of injecting a high-frequency signal according to the voltage error model; Based on the current polarity of the target motor in the zero current region and the voltage error curve when the high-frequency signal is injected, determine the voltage value of the target motor to be compensated when the high-frequency signal is injected, and perform voltage compensation on the target motor when the high-frequency signal is injected according to the voltage value; The step of establishing a discrete prediction model of the target motor based on the electrical parameters comprises: Normalizing the electrical parameters, using the normalized electrical parameters as input data of the single-layer neural network, and using the predicted value of the target motor current as output data of the single-layer neural network; The input data and the output data are simultaneously input into a single-layer neural network and trained using a gradient descent method to obtain a discrete prediction model of the target motor; Among them, the established discrete prediction model is fitted based on the adaptability of the neural network to improve the robustness of the discrete prediction model; The influencing factors include at least one of the following: dead time, conduction judgment time, switch tube conduction voltage drop and parasitic capacitance; The mid-base frequency part and the high frequency part of the voltage error model are decoupled to determine the high frequency component expression of the voltage error, and based on the high frequency component expression, the voltage error curve determined by the voltage error model is corrected; The closed-loop control of the target motor includes: After the high-frequency signal is injected into the target motor, the fault feature quantity of the target motor is extracted by a preset convex optimization extraction scheme; Based on the extracted fault feature quantity, and taking the speed and rotor position estimated by the monitoring prediction algorithm of the target motor as independent variables, an objective function is established. If the objective function is a convex function, the independent variables are input into the objective function as input values, so as to prompt the objective function to perform solution calculation and obtain the objective function value of the objective function; When a target function value in any time unit exceeds a preset threshold, determining a rotor position estimated by a monitoring prediction algorithm of the target motor at this time, and performing a rotor position correction on a control system of the target motor based on the estimated rotor position; After the control system of the target motor is modified, the phase angle of the two-phase current envelope of the target motor is obtained by using a preset envelope extraction scheme, and the phase angle is filtered; Determine the rotor position based on the filtered phase angle, and enable the control system to perform closed-loop control on the target motor based on the rotor position to improve the stability of the target motor operation; Wherein, whether the objective function is a convex function is determined based on the Hessian matrix eigenvalue of the objective function; Wherein, a threshold is set based on an estimation error of a monitoring estimation algorithm, and the threshold is corrected according to a time-varying characteristic of a target motor control system parameter; Wherein, obtaining the objective function value of the objective function includes: calculating the operation status of the target motor monitoring estimation algorithm in real time through the objective function to obtain the objective function value; Among them, the estimation accuracy of the monitoring estimation algorithm is compensated after eliminating the influence of the hysteresis and cross saturation of the current sampling in the target motor on the position estimation; Wherein, the preset envelope extraction scheme is established according to the peak holding circuit principle of the target motor; The method of obtaining the phase angle of the two-phase current envelope of the target motor by using a preset envelope extraction scheme includes: extracting the low-frequency component associated with the motor speed in the high-frequency current response of the target motor by using the preset envelope extraction scheme, determining the two-phase current envelope of the target motor for the low-frequency component, normalizing the two-phase current envelope, determining the sine and cosine values ​​of the two-phase current envelope, and calculating the sine and cosine values ​​by an inverse tangent function to obtain the phase angle of the two-phase current envelope of the target motor; Among them, the objective function is designed with the estimated speed and rotor position as independent variables. The Hessian matrix eigenvalue of the objective function is used to determine that when the objective function is a convex function, the global minimum occurs when the estimated value is equal to the true value. The operation status is determined by monitoring the estimation algorithm through real-time calculation of the objective function. When the objective function value exceeds the set threshold, erroneous convergence of the position estimation occurs. The position estimation is corrected to make the target motor re-enter a stable state.

3. A computer device, characterized in that: include: one or more processors; a processor for executing one or more programs; When the one or more programs are executed by the one or more processors, the method according to claim 1 is implemented.

4. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed, the method according to claim 1 is implemented.

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