A permanent magnet synchronous motor control method and system based on improved particle swarm

Abstract

The present application provides a permanent magnet synchronous motor control method and system, equipment, and medium based on an improved particle swarm, the method comprising: constructing a two-phase rotating coordinate system according to the three-phase stator winding of the permanent magnet synchronous motor, and constructing a motion model of the permanent magnet synchronous motor in the two-phase rotating coordinate system; constructing a current control model of the permanent magnet synchronous motor based on a sliding mode control algorithm according to the motion model of the permanent magnet synchronous motor, the current control model including several sliding mode control parameters to be optimized; optimizing the sliding mode control parameters of the current control model based on an adaptive particle swarm optimization algorithm to obtain the optimal sliding mode control parameters; and performing sliding mode control on the permanent magnet synchronous motor according to the optimal sliding mode control parameters. The present application utilizes an improved particle swarm intelligent algorithm, which can have stronger global search and local optimization characteristics, accelerate the speed at which the system reaches the sliding mode surface, and improve the dynamic performance of the system.

Description

Technical Field

[0001] The present application relates to the technical field of flywheel energy storage systems, and in particular to a permanent magnet synchronous motor control method and system, equipment, and medium based on an improved particle swarm. Background Art

[0002] With the rapid development of renewable energy technologies such as wind power and solar power generation, renewable energy will occupy an extremely important position in the energy structure of future power systems. However, renewable energy itself is intermittent and random. If it is directly connected to the power system, it will seriously affect the power quality of the power system. Energy storage technology is one of the important ways to solve this problem and efficiently utilize renewable energy. It is an indispensable part of the development of future smart grids.

[0003] As an emerging physical energy storage system in recent years, the flywheel energy storage system has broad application prospects in improving the access capacity of wind power. Since the energy storage of the flywheel energy storage system is mainly in the rotating flywheel rotor, its energy conversion mainly relies on the variable frequency speed regulation of the flywheel motor, which requires accurate information about the rotor position and speed. Since most flywheel motors use high-speed permanent magnet synchronous motors, which are typical multivariable and strongly coupled nonlinear systems, in actual operation, although the traditional PI controller can meet the requirements of the control system, the controller is very sensitive to external and internal interference, resulting in limited control performance and deviation from the expected control target. Sliding mode control has the characteristics of simple design, simple control algorithm, and insensitivity to parameter changes and external disturbance changes. It has gradually been applied to permanent magnet synchronous motor control. However, due to the discontinuity of the switching function in sliding mode control, severe jitter will occur, thereby exacerbating the power loss of the system. To address this problem, the particle swarm optimization algorithm can be used to achieve system optimization of sliding mode control. However, the traditional particle swarm optimization algorithm completes system optimization by following the individual optimal value and the group optimal value. Although it has the advantages of simple operation and fast convergence speed, it is easy to fall into the defects of local optimization and weak analysis ability when facing nonlinear and highly complex objects, which makes it difficult to completely solve nonlinear optimization problems. Summary of the invention

[0004] In view of this, the present application proposes a permanent magnet synchronous motor control method and system, equipment, and medium based on an improved particle swarm. By utilizing an improved particle swarm intelligent algorithm, it can have stronger global search and local optimization characteristics, so as to optimize the sliding mode control parameters, speed up the system to reach the sliding mode surface, eliminate the high-frequency jitter phenomenon inherent in the sliding mode controller, and improve the dynamic performance of the system.

[0005] In a first aspect, the present application provides a permanent magnet synchronous motor control method based on an improved particle swarm, comprising:

[0006] A two-phase rotating coordinate system is constructed according to the three-phase stator winding of the permanent magnet synchronous motor, and a motion model of the permanent magnet synchronous motor in the two-phase rotating coordinate system is constructed;

[0007] According to the motion model of the permanent magnet synchronous motor, a current control model of the permanent magnet synchronous motor is constructed based on a sliding mode control algorithm, wherein the current control model includes a plurality of sliding mode control parameters to be optimized;

[0008] Optimizing the sliding mode control parameters of the current control model based on an adaptive particle swarm optimization algorithm to obtain optimal sliding mode control parameters;

[0009] According to the optimal sliding mode control parameters, sliding mode control is performed on the permanent magnet synchronous motor.

[0010] From the above, the present application provides a permanent magnet synchronous motor control method based on an improved particle swarm, obtains an adaptive particle swarm optimization algorithm by improving the traditional particle swarm optimization algorithm, and optimizes the sliding mode control parameters of the permanent magnet synchronous motor using the adaptive particle swarm optimization algorithm to obtain the optimal sliding mode control parameters, thereby achieving optimal control of the permanent magnet synchronous motor. The present application adopts an adaptive particle swarm optimization algorithm that can have stronger global search and local optimization characteristics, so that the sliding mode control has high control accuracy, no overshoot, and strong robustness to external load disturbances.

[0011] Optionally, constructing a motion model of the permanent magnet synchronous motor in the two-phase rotating coordinate system includes:

[0012] According to the two-phase rotating coordinate system, the voltage equation of the permanent magnet synchronous motor in the two-phase rotating coordinate system is constructed:

[0013]

[0014] Electromagnetic torque T e The expression is:

[0015]

[0016] When the permanent magnet synchronous motor is of hidden pole type, if the d-axis inductance is consistent with the q-axis inductance, the electromagnetic torque T e The expression is:

[0017]

[0018] The motion model of the permanent magnet synchronous motor is:

[0019]

[0020] Among them, i d and i qis the stator current of the dq axis of the rotating coordinate system, u d and u q is the stator voltage of the dq axis in the rotating two-phase coordinate system, p is the number of pole pairs, L is the equivalent inductance of the generator, and L d and L q is the dq axis inductance, R is the stator resistance, k M is the electromagnetic torque coefficient, f is the flux linkage of the permanent magnet, J is the moment of inertia, ω r is the angular velocity of the motor rotor.

[0021] As shown above, the permanent magnet synchronous motor consists of permanent magnets installed on the rotor surface and three-phase stator windings. The three windings are sinusoidal curves. According to the voltage equation and electromagnetic torque expression of the permanent magnet synchronous motor in the two-phase rotating coordinate system, the motion model of the permanent magnet synchronous motor in the two-phase rotating coordinate system can be constructed, so as to construct a control model based on the sliding mode control algorithm according to the motion module.

[0022] Optionally, the current control model of the permanent magnet synchronous motor based on the sliding mode control algorithm includes:

[0023] According to the sliding mode control algorithm, the state variables in the permanent magnet synchronous motor are defined as:

[0024]

[0025]

[0026] Among them, ω ref is the target set angular velocity of the motor rotor of the permanent magnet synchronous motor;

[0027] An integer-order sliding surface is used, and the sliding surface is defined as:

[0028]

[0029] Introducing the traditional exponential approximation rule into the sliding surface, the sliding surface expression can be obtained as follows:

[0030]

[0031] According to the expression of the sliding surface, the current control model of the q-axis of the permanent magnet synchronous motor is constructed as follows:

[0032]

[0033] Wherein, c, ε, q are several sliding mode control parameters to be optimized in the current control model.

[0034] From the above, based on the motion model of the permanent magnet synchronous motor constructed above, the present application adopts a sliding mode control algorithm to implement the sliding mode control of the permanent magnet synchronous motor, thereby effectively improving the control accuracy and anti-interference ability of the system, and constructs a current control model of the q-axis of the permanent magnet synchronous motor according to the sliding mode control algorithm. The current control model contains an integral term, which can reduce the jitter phenomenon on the one hand, and eliminate the steady-state error of the system on the other hand. The control quality of the system will be improved.

[0035] Optionally, the adaptive particle swarm optimization algorithm includes:

[0036] The inertia weight ω in the particle swarm optimization algorithm is improved by adopting a nonlinear decreasing strategy. The expression of the improved adaptive inertia weight ω is:

[0037]

[0038] Among them, k is the current iteration number, k max is the maximum number of iterations, ω max and ω min is the set value;

[0039] According to the improved adaptive inertia weight ω, the particle velocity update formula of the adaptive particle swarm optimization algorithm is obtained as follows:

[0040]

[0041] in,

[0042] η is a coefficient between 0 and 1, c1 is the individual learning factor, c2 is the social learning factor, r1 and r2 are random numbers between 0 and 1, and are the position and velocity of the particle, and They are individual extreme values ​​and global extreme values ​​respectively.

[0043] From the above, the present application improves the particle swarm optimization algorithm by adopting a nonlinear decreasing strategy for the inertia weight, changing it from the maximum value to the minimum value, so as to improve the early global search capability and the later local search capability, realize the adaptive iteration of the inertia weight, and obtain the particle speed update formula of the adaptive particle swarm optimization algorithm based on the adaptive inertia weight, and by adjusting the ratio of the personal learning factor to the social learning factor in the speed update formula, the search speed can be accelerated.

[0044] Optionally, the optimizing the sliding mode control parameters of the current control model based on the adaptive particle swarm optimization algorithm includes:

[0045] Allocate particles to the sliding mode control parameters c, ε, q to be optimized in the current control model in sequence, and continuously iterate and update the particle swarm velocity using an adaptive particle swarm optimization algorithm;

[0046] The integral time weighted absolute error IATE is used as a performance evaluation index, and the optimal sliding mode control parameters c, ε, q are obtained according to the performance evaluation index; the expression of the integral time weighted absolute error IATE is:

[0047]

[0048] From the above, by adopting the integrated time weighted absolute error IATE as the performance evaluation index, the adaptive particle swarm optimization algorithm is used to find the optimal sliding mode control parameters, so as to realize the optimal control of the permanent magnet synchronous motor by using the optimal sliding mode control parameters.

[0049] In a second aspect, the present application provides a permanent magnet synchronous motor control system based on an improved particle swarm, comprising:

[0050] A construction module: used to construct a two-phase rotating coordinate system according to the three-phase stator winding of the permanent magnet synchronous motor, and to construct a motion model of the permanent magnet synchronous motor in the two-phase rotating coordinate system;

[0051] The building module is also used to build a current control model of the permanent magnet synchronous motor based on a sliding mode control algorithm according to the motion model of the permanent magnet synchronous motor, and the current control model includes a plurality of sliding mode control parameters to be optimized;

[0052] Optimization module: used for optimizing the sliding mode control parameters of the current control model based on an adaptive particle swarm optimization algorithm to obtain the optimal sliding mode control parameters;

[0053] A control module is used to perform sliding mode control on the permanent magnet synchronous motor according to the optimal sliding mode control parameters.

[0054] In a third aspect, the present application provides a computing device, the computing device comprising:

[0055] processor;

[0056] A memory for storing one or more programs;

[0057] When the one or more programs are executed by the processor, the processor implements the above-mentioned permanent magnet synchronous motor control method based on improved particle swarm.

[0058] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a computer, implements the above-mentioned permanent magnet synchronous motor control method based on improved particle swarm.

[0059] These and other aspects of the present application will become more apparent from the following description of the embodiment(s). BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 A flowchart of a permanent magnet synchronous motor control method based on an improved particle swarm provided in an embodiment of the present application;

[0061] Figure 2 A flowchart of an adaptive particle swarm optimization provided in an embodiment of the present application;

[0062] Figure 3 A schematic diagram of a permanent magnet synchronous motor control based on an improved particle swarm provided in an embodiment of the present application;

[0063] Figure 4 A structural diagram of a permanent magnet synchronous motor control system based on an improved particle swarm provided in an embodiment of the present application;

[0064] Figure 5 A structural diagram of a computing device provided in an embodiment of the present application.

[0065] It should be understood that the size and shape of each block diagram in the above structural diagram are for reference only and should not constitute an exclusive interpretation of the embodiments of the present application. The relative positions and inclusion relationships between the blocks presented in the structural diagram are only schematic representations of the structural associations between the blocks, and do not limit the physical connection methods of the embodiments of the present application. DETAILED DESCRIPTION

[0066] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be further described in detail below with reference to the accompanying drawings.

[0067] Based on the control problems existing in the existing permanent magnet synchronous motors, the embodiment of the present application constructs a motion model of the permanent magnet synchronous motor according to a two-phase rotating coordinate system, and constructs a current control model of the permanent magnet synchronous motor based on the motion model using a sliding mode control algorithm. Then, an adaptive particle swarm optimization algorithm is used to continuously optimize the sliding mode control parameters in the current control model, so that the sliding mode control process of the permanent magnet synchronous motor gradually tends to be stable, thereby achieving high-precision control of the permanent magnet synchronous motor.

[0068] like Figure 1 As shown, the embodiment of the present application provides a permanent magnet synchronous motor control method based on an improved particle swarm, referring to Figure 1 , the method comprising:

[0069] S10: constructing a two-phase rotating coordinate system according to the three-phase stator winding of the permanent magnet synchronous motor, and constructing a motion model of the permanent magnet synchronous motor in the two-phase rotating coordinate system;

[0070] The permanent magnet synchronous motor in this step consists of a permanent magnet mounted on the rotor surface and three-phase stator windings. The three stator windings are sinusoidal curves. Assuming that the windings are undamped, the flux saturation, eddy current, hysteresis current loss and current field dynamics can be ignored, and the induced electromotive force is sinusoidal. Then the voltage equation of the permanent magnet synchronous motor in this two-phase rotating coordinate system is:

[0071]

[0072] Electromagnetic torque T e The expression is:

[0073]

[0074] When the permanent magnet synchronous motor is of hidden pole type, if the d-axis inductance is consistent with the q-axis inductance, the electromagnetic torque T of formula (2) is e The expression can be rewritten as:

[0075]

[0076] The motion model of the permanent magnet synchronous motor is:

[0077]

[0078] Among them, i d and i q is the stator current of the dq axis of the rotating coordinate system, u d and u q is the stator voltage of the dq axis in the rotating two-phase coordinate system, p is the number of pole pairs, L is the equivalent inductance of the generator, and L d and L q is the dq axis inductance, R is the stator resistance, k M is the electromagnetic torque coefficient, f is the flux linkage of the permanent magnet, J is the moment of inertia, ω r is the angular velocity of the motor rotor.

[0079] S20: constructing a current control model of the permanent magnet synchronous motor based on a sliding mode control algorithm according to the motion model of the permanent magnet synchronous motor, wherein the current control model includes a plurality of sliding mode control parameters to be optimized;

[0080] Due to the nonlinearity of the permanent magnet synchronous motor, the traditional PI control cannot meet the requirements of the system. Based on this, in the embodiment of the present application, the sliding mode control is used to replace the traditional PI speed controller in the vector control of the permanent magnet synchronous motor, thereby effectively improving the control accuracy and anti-interference ability of the system. According to the sliding mode control algorithm, the state variable in the permanent magnet synchronous motor is defined as:

[0081]

[0082]

[0083] Among them, ω ref is the target set angular velocity of the motor rotor of the permanent magnet synchronous motor, usually a constant value, ω r is the actual angular velocity of the motor rotor;

[0084] An integer-order sliding surface is used, and the sliding surface is defined as:

[0085]

[0086] The traditional exponential approximation rule is:

[0087]

[0088] Introducing the traditional exponential approximation rule of the above formula (8) into the sliding surface of the above formula (7) yields the expression of the sliding surface:

[0089]

[0090] According to the above sliding surface expression (9), the current control model of the q-axis of the permanent magnet synchronous motor is constructed as follows:

[0091]

[0092] Wherein, c, ε, q are several sliding mode control parameters to be optimized in the current control model.

[0093] The current control model of the q-axis of the permanent magnet synchronous motor constructed in this step contains an integral term, which can reduce the vibration phenomenon on the one hand and eliminate the steady-state error of the system on the other hand. The control quality of the system will be improved. When the sliding mode reaches the set condition, the system gradually stabilizes under the action of the controller.

[0094] S30: Optimizing the sliding mode control parameters of the current control model based on an adaptive particle swarm optimization algorithm to obtain optimal sliding mode control parameters;

[0095] PSO (Particle Swarm Optimization) algorithm is robust in solving continuous nonlinear optimization problems. In the particle swarm optimization algorithm, each solution is a "bird" in the search space, which is called a "particle". The group is modeled as particles in a multidimensional space with position and velocity. These particles have two basic capabilities: remembering their best position and understanding the best position of the world. Group members exchange good positions and adjust their positions and velocities according to the appropriate positions.

[0096] The general steps of particle swarm optimization are as follows:

[0097] a: Initialize the particle speed and position in the particle swarm, calculate the fitness value of each particle, and select the local extreme value and the global extreme value;

[0098] b: The particle velocity position will be updated according to the particle local and global extreme values;

[0099] c: Repeat step b until the maximum number of iterations is reached or the error converges.

[0100] In the above optimization process, the particle velocity position is updated as follows:

[0101] v id =ωv id +c1r1(p id -x id )+c2r2(pgd-x id ) (11)

[0102] x id =x id +v id (12)

[0103] Among them, ω is the inertia weight, c1 is the individual learning factor, c2 is the social learning factor, r1 and r2 are random numbers between 0 and 1, and are the position and velocity of the particle, and They are individual extreme values ​​and global extreme values ​​respectively.

[0104] Since the traditional particle swarm optimization algorithm completes system optimization by following the individual optimal value and the group optimal value, although it has the advantages of simple operation and fast convergence speed, it is easy to fall into the defects of local optimization and weak analysis ability when facing nonlinear and high-complexity objects, which makes it difficult to completely solve nonlinear optimization problems. In order to realize the adaptive parameter setting of the particle swarm optimization algorithm and avoid the algorithm from falling into local optimization, the embodiment of the present application proposes a new adaptive particle swarm optimization algorithm from the perspective of adaptive inertia weight and particle velocity update formula.

[0105] Among them, the inertia weight ω in the particle swarm optimization algorithm is improved by adopting a nonlinear decreasing strategy. The expression of the improved adaptive inertia weight ω is:

[0106]

[0107] Among them, k is the current iteration number, k max is the maximum number of iterations, ω max and ω min is the set value. According to experimental results, ω max The value is 0.8, ω minWhen the value is 0.3, the adaptive particle swarm optimization algorithm can have stronger search performance.

[0108] In the particle speed update formula of the particle swarm optimization algorithm, the first term represents the inertial speed of the particle, the second term represents self-experience learning, and the third term represents social experience. In order to improve the search performance of the algorithm, according to the improved adaptive inertia weight ω, the particle speed update formula of the adaptive particle swarm optimization algorithm can be obtained as follows:

[0109]

[0110] in,

[0111] η is a coefficient between 0 and 1. By adjusting the ratio of the individual learning factor to the social learning factor in the speed update formula, the search speed can be accelerated. In the early iteration of the speed update formula, η gradually increases and gradually decreases in the later iteration. Figure 2 As shown in the figure, in the initial stage of PSO search, particles can search for possible optimal solutions in the solution space, and other particles can move to the edge solution space as quickly as possible through information exchange between groups. Therefore, the adaptive particle swarm optimization algorithm can enable particles to move to the possible optimal solution as quickly as possible and search nearby to improve the accuracy of the algorithm.

[0112] Based on the adaptive particle swarm optimization algorithm of the above formula (14), the sliding mode control parameters of the current control model constructed in step S20 can be optimized. The optimization process includes:

[0113] Allocate particles to the sliding mode control parameters c, ε, q to be optimized in the current control model in sequence, and continuously iterate and update the particle swarm velocity using an adaptive particle swarm optimization algorithm;

[0114] The integral time weighted absolute error IATE is used as a performance evaluation index, and the optimal sliding mode control parameters c, ε, q are obtained according to the performance evaluation index; the expression of the integral time weighted absolute error IATE is:

[0115]

[0116] S40: Performing sliding mode control on the permanent magnet synchronous motor according to the optimal sliding mode control parameters.

[0117] like Figure 3 FIG. 1 is a schematic diagram of a permanent magnet synchronous motor control based on an improved particle swarm according to an embodiment of the present application, referring to FIG. Figure 3When the flywheel energy storage system is performing charge and discharge control, the permanent magnet synchronous motor control based on the improved particle swarm of the embodiment of the present application can be used to iteratively optimize the sliding mode control parameters, and the optimized sliding mode control parameters can be substituted into the current control model to obtain the q-axis control current of the permanent magnet synchronous motor, and the q-axis control current can be used to control the permanent magnet synchronous motor. By continuously iteratively optimizing the sliding mode control parameters until the control of the permanent magnet synchronous motor reaches the global optimum, the optimal sliding mode control in the charge and discharge process of the flywheel energy storage system is realized, thereby providing stability of the flywheel energy storage system.

[0118] In summary, the embodiment of the present application provides a permanent magnet synchronous motor control method based on an improved particle swarm, improves the traditional particle swarm optimization algorithm from the perspective of adaptive inertia weight and particle velocity update formula to obtain an adaptive particle swarm optimization algorithm, and uses the adaptive particle swarm optimization algorithm to optimize the sliding mode control parameters of the permanent magnet synchronous motor to obtain the optimal sliding mode control parameters, thereby achieving optimal control of the permanent magnet synchronous motor. The embodiment of the present application adopts an adaptive particle swarm optimization algorithm that can have stronger global search and local optimization characteristics, so that the sliding mode control has high control accuracy, no overshoot, and strong robustness to external load disturbances.

[0119] Figure 4 The structure diagram of a permanent magnet synchronous motor control system based on an improved particle swarm provided in an embodiment of the present application can be used to implement any step of the above-mentioned permanent magnet synchronous motor control method based on an improved particle swarm and its optional embodiments. Figure 4 As shown, the system includes a construction module 210, an optimization module 220 and a control module 230;

[0120] Among them, the construction module 210 is used to construct a two-phase rotating coordinate system according to the three-phase stator winding of the permanent magnet synchronous motor, and construct a motion model of the permanent magnet synchronous motor in the two-phase rotating coordinate system; the construction module 210 is also used to construct a current control model of the permanent magnet synchronous motor based on the sliding mode control algorithm according to the motion model of the permanent magnet synchronous motor, and the current control model includes several sliding mode control parameters to be optimized; the optimization module 220 is used to optimize the sliding mode control parameters of the current control model based on an adaptive particle swarm optimization algorithm to obtain the optimal sliding mode control parameters; the control module 230 is used to perform sliding mode control on the permanent magnet synchronous motor according to the optimal sliding mode control parameters.

[0121] It should be understood that the device or module in the embodiment of the present application can be implemented by software, for example, it can be implemented by a computer program or instruction having the above functions, and the corresponding computer program or instruction can be stored in a memory inside the terminal, and the processor reads the corresponding computer program or instruction inside the memory to implement the above functions. Alternatively, the device or module in the embodiment of the present application can also be implemented by hardware. Alternatively, the device or module in the embodiment of the present application can also be implemented by a combination of a processor and a software module.

[0122] It should be understood that the processing details of the device or module in the embodiment of the present application can refer to Figure 1-Figure 3 The related descriptions of the illustrated embodiment and related extended embodiments will not be repeated in the embodiments of the present application.

[0123] Figure 5 1 is a structural diagram of a computing device 1000 provided in an embodiment of the present application. The computing device 1000 includes: a processor 1010 , a memory 1020 , a communication interface 1030 , and a bus 1040 .

[0124] It should be understood that Figure 5 The communication interface 1030 in the computing device 1000 shown may be used to communicate with other devices.

[0125] The processor 1010 may be connected to a memory 1020. The memory 1020 may be used to store the program code and data. Therefore, the memory 1020 may be a storage unit inside the processor 1010, or an external storage unit independent of the processor 1010, or a component including a storage unit inside the processor 1010 and an external storage unit independent of the processor 1010.

[0126] Optionally, the computing device 1000 may further include a bus 1040. The memory 1020 and the communication interface 1030 may be connected to the processor 1010 via the bus 1040. The bus 1040 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus 1040 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 The fact that only one line is used in the diagram does not mean that there is only one bus or only one type of bus.

[0127] It should be understood that in the embodiment of the present application, the processor 1010 may adopt a central processing unit (CPU). The processor may also be 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. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. Alternatively, the processor 1010 may adopt one or more integrated circuits to execute relevant programs to implement the technical solutions provided in the embodiment of the present application.

[0128] The memory 1020 may include a read-only memory and a random access memory, and provides instructions and data to the processor 1010. A portion of the processor 1010 may also include a nonvolatile random access memory. For example, the processor 1010 may also store information on the device type.

[0129] When the computing device 1000 is running, the processor 1010 executes the computer-executable instructions in the memory 1020 to perform the operating steps of the above method.

[0130] It should be understood that the computing device 1000 according to the embodiment of the present application can correspond to the corresponding subject in the method according to each embodiment of the present application, and the above-mentioned other operations and / or functions of each module in the computing device 1000 are respectively for realizing the corresponding process of each method of the present embodiment, which will not be repeated here for the sake of brevity.

[0131] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0132] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0133] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0134] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0135] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0136] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0137] An embodiment of the present application also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, it is used to execute the above method, which includes at least one of the solutions described in the above embodiments.

[0138] The computer storage medium of the embodiment of the present application can adopt any combination of one or more computer-readable media. Computer-readable media can be computer-readable signal media or computer-readable storage media. Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or devices, or any combination of the above. More specific examples (non-exhaustive lists) of computer-readable storage media include: electrical connections with one or more wires, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this document, computer-readable storage media can be any tangible medium containing or storing programs, which can be used by instruction execution systems, devices or devices or used in combination with them.

[0139] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0140] The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0141] Computer program code for performing the operation of the present application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., using an Internet service provider to connect through the Internet).

[0142] It should be noted that the embodiments described in the present application are only a part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application usually described and shown in the drawings can be arranged and designed in various different configurations. Therefore, the above detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0143] The words "first, second, third, etc." or module A, module B, module C and other similar terms in the specification and claims are only used to distinguish similar objects and do not represent a specific ordering of the objects. It is understandable that the specific order or sequence can be interchanged where permitted so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0144] In the above description, the numbers representing the steps involved do not necessarily mean that the steps will be executed. Intermediate steps may be included or replaced by other steps. The order of the previous and next steps may be interchanged or executed simultaneously if permitted.

[0145] The term "comprising" as used in the description and claims should not be interpreted as being limited to what is listed thereafter; it does not exclude other elements or steps. Therefore, it should be interpreted as specifying the presence of the features, integers, steps or components mentioned, but does not exclude the presence or addition of one or more other features, integers, steps or components and groups thereof. Therefore, the expression "a device comprising means A and B" should not be limited to a device consisting of components A and B only.

[0146] The "one embodiment" or "embodiment" mentioned in this specification means that the specific features, structures or characteristics described in conjunction with the embodiment are included in at least one embodiment of the present application. Therefore, the terms "in one embodiment" or "in an embodiment" appearing in various places in this specification do not necessarily refer to the same embodiment, but may refer to the same embodiment. In addition, in the various embodiments of the present application, if there is no special explanation and logical conflict, the terms and / or descriptions between different embodiments are consistent and can be referenced to each other, and the technical features in different embodiments can be combined to form a new embodiment according to their inherent logical relationship.

[0147] Note that the above are only preferred embodiments of the present application and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present application is described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may also include more other equivalent embodiments without departing from the concept of the present invention, all of which belong to the protection scope of the present invention.

Claims

1. A permanent magnet synchronous motor control method based on improved particle swarm, characterized in that: include: A two-phase rotating coordinate system is constructed according to the three-phase stator winding of the permanent magnet synchronous motor, and a motion model of the permanent magnet synchronous motor in the two-phase rotating coordinate system is constructed; The construction of the motion model of the permanent magnet synchronous motor in the two-phase rotating coordinate system comprises: According to the two-phase rotating coordinate system, the voltage equation of the permanent magnet synchronous motor in the two-phase rotating coordinate system is constructed: Electromagnetic torque T e The expression is: When the permanent magnet synchronous motor is of hidden pole type, if the d-axis inductance is consistent with the q-axis inductance, the electromagnetic torque T e The expression is: The motion model of the permanent magnet synchronous motor is: Among them, i d and i q is the stator current of the dq axis of the rotating coordinate system, u d and u q is the stator voltage of the dq axis in the rotating two-phase coordinate system, p is the number of pole pairs, L is the equivalent inductance of the generator, and L d and L q is the dq axis inductance, R is the stator resistance, k M is the electromagnetic torque coefficient, ψ f is the flux linkage of the permanent magnet, J is the moment of inertia, ω r is the angular velocity of the motor rotor; According to the motion model of the permanent magnet synchronous motor, a current control model of the permanent magnet synchronous motor is constructed based on a sliding mode control algorithm, wherein the current control model includes a plurality of sliding mode control parameters to be optimized; the current control model of the permanent magnet synchronous motor constructed based on the sliding mode control algorithm includes: According to the sliding mode control algorithm, the state variables in the permanent magnet synchronous motor are defined as: Among them, ω ref is the target set angular velocity of the motor rotor of the permanent magnet synchronous motor; An integer-order sliding surface is used, and the sliding surface is defined as: Introducing the traditional exponential approximation rule into the sliding surface, the sliding surface expression can be obtained as follows: According to the expression of the sliding surface, the current control model of the q-axis of the permanent magnet synchronous motor is constructed as follows: Wherein, c, ε, q are several sliding mode control parameters to be optimized in the current control model; Optimizing the sliding mode control parameters of the current control model based on an adaptive particle swarm optimization algorithm to obtain optimal sliding mode control parameters; The optimizing the sliding mode control parameters of the current control model based on the adaptive particle swarm optimization algorithm includes: Allocating particles to the sliding mode control parameters c, ε, q to be optimized in the current control model in turn, using an adaptive particle swarm optimization algorithm to continuously iterate and update the particle swarm velocity, and obtaining the optimal sliding mode control parameters c, ε, q according to the performance evaluation index; The adaptive particle swarm optimization algorithm includes: The inertia weight ω in the particle swarm optimization algorithm is improved by adopting a nonlinear decreasing strategy. The expression of the improved adaptive inertia weight ω is: Among them, k is the current iteration number, k max is the maximum number of iterations, ω max and ω min is the set value; According to the improved adaptive inertia weight ω, the particle velocity update formula of the adaptive particle swarm optimization algorithm is obtained as follows: in, η is a coefficient between 0 and 1, c1 is the individual learning factor, c2 is the social learning factor, r1 and r2 are random numbers between 0 and 1, and are the position and velocity of the particle, and They are individual extreme values ​​and global extreme values ​​respectively; According to the optimal sliding mode control parameters, sliding mode control is performed on the permanent magnet synchronous motor.

2. The method according to claim 1, characterized in that The optimizing the sliding mode control parameters of the current control model based on the adaptive particle swarm optimization algorithm includes: Allocate particles to the sliding mode control parameters c, ε, q to be optimized in the current control model in sequence, and continuously iterate and update the particle swarm velocity using an adaptive particle swarm optimization algorithm; The integral time weighted absolute error IATE is used as a performance evaluation index, and the optimal sliding mode control parameters c, ε, q are obtained according to the performance evaluation index; the expression of the integral time weighted absolute error IATE is:

3. A permanent magnet synchronous motor control system based on improved particle swarm, characterized in that: include: A construction module: used to construct a two-phase rotating coordinate system according to the three-phase stator winding of the permanent magnet synchronous motor, and to construct a motion model of the permanent magnet synchronous motor in the two-phase rotating coordinate system; The construction of the motion model of the permanent magnet synchronous motor in the two-phase rotating coordinate system comprises: According to the two-phase rotating coordinate system, the voltage equation of the permanent magnet synchronous motor in the two-phase rotating coordinate system is constructed: Electromagnetic torque T e The expression is: When the permanent magnet synchronous motor is of hidden pole type, if the d-axis inductance is consistent with the q-axis inductance, the electromagnetic torque T e The expression is: The motion model of the permanent magnet synchronous motor is: Among them, i d and i q is the stator current of the dq axis of the rotating coordinate system, u d and u q is the stator voltage of the dq axis in the rotating two-phase coordinate system, p is the number of pole pairs, L is the equivalent inductance of the generator, and L d and L q is the dq axis inductance, R is the stator resistance, k M is the electromagnetic torque coefficient, ψ f is the flux linkage of the permanent magnet, J is the moment of inertia, ω r is the angular velocity of the motor rotor; The construction module is also used to construct a current control model of the permanent magnet synchronous motor based on a sliding mode control algorithm according to the motion model of the permanent magnet synchronous motor, and the current control model includes a number of sliding mode control parameters to be optimized; the current control model of the permanent magnet synchronous motor based on the sliding mode control algorithm includes: According to the sliding mode control algorithm, the state variables in the permanent magnet synchronous motor are defined as: Among them, ω ref is the target set angular velocity of the motor rotor of the permanent magnet synchronous motor; An integer-order sliding surface is used, and the sliding surface is defined as: Introducing the traditional exponential approximation rule into the sliding surface, the sliding surface expression can be obtained as follows: According to the expression of the sliding surface, the current control model of the q-axis of the permanent magnet synchronous motor is constructed as follows: Wherein, c, ε, q are several sliding mode control parameters to be optimized in the current control model; Optimization module: used for optimizing the sliding mode control parameters of the current control model based on an adaptive particle swarm optimization algorithm to obtain optimal sliding mode control parameters; The optimizing the sliding mode control parameters of the current control model based on the adaptive particle swarm optimization algorithm includes: Allocating particles to the sliding mode control parameters c, ε, q to be optimized in the current control model in turn, using an adaptive particle swarm optimization algorithm to continuously iterate and update the particle swarm velocity, and obtaining the optimal sliding mode control parameters c, ε, q according to the performance evaluation index; The adaptive particle swarm optimization algorithm includes: The inertia weight ω in the particle swarm optimization algorithm is improved by adopting a nonlinear decreasing strategy. The expression of the improved adaptive inertia weight ω is: Among them, k is the current iteration number, k max is the maximum number of iterations, ω max and ω min is the set value; According to the improved adaptive inertia weight ω, the particle velocity update formula of the adaptive particle swarm optimization algorithm is obtained as follows: in, η is a coefficient between 0 and 1, c1 is the individual learning factor, c2 is the social learning factor, r1 and r2 are random numbers between 0 and 1, and are the position and velocity of the particle, and They are individual extreme values ​​and global extreme values ​​respectively; A control module is used to perform sliding mode control on the permanent magnet synchronous motor according to the optimal sliding mode control parameters.

4. A computing device, characterized in that include: processor; A memory for storing one or more programs; When the one or more programs are executed by the processor, the processor implements a permanent magnet synchronous motor control method based on improved particle swarm as described in any one of claims 1 to 2.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a computer, the permanent magnet synchronous motor control method based on improved particle swarm as described in any one of claims 1 to 2 is implemented.

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