Permanent magnet synchronous motor control method and system based on super helical sliding mode observer

Abstract

The present application provides a permanent magnet synchronous motor control method and system based on a super-helical sliding mode observer, the method comprising: constructing a two-phase stationary coordinate system according to the three-phase stator winding of the permanent magnet synchronous motor, and obtaining the stator current and stator voltage in the two-phase stationary coordinate system; obtaining the back electromotive force prediction value of the super-helical sliding mode observer according to the sliding mode observer and the super-helical sliding mode control algorithm; smoothing the back electromotive force prediction value using a Gaussian error function to weaken the system jitter of the sliding mode control; obtaining the rotor speed prediction value and rotor position prediction value of the permanent magnet synchronous motor according to the smoothed back electromotive force prediction value. The present application adopts super-helical sliding mode control to effectively reduce the jitter phenomenon in traditional sliding mode control, so that the permanent magnet synchronous motor in the flywheel system can accurately estimate the position and speed of the rotor during charging and discharging, thereby improving the stability 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 a super-helical sliding mode observer. 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 wind power access capabilities. Since the energy storage of the flywheel energy storage system is mainly in the rotating flywheel rotor, its energy conversion is mainly achieved by 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, one existing method is to obtain the speed and position information of the motor through mechanical sensors, but due to factors such as the installation location and geographical environment, mechanical sensors will bring a series of installation and maintenance problems. Another way is to obtain the position of the motor rotor by using a sliding mode observer algorithm, but the existing sliding mode control is accompanied by high-frequency jitter in the sliding mode, and the rotor position estimation easily causes the error of high-frequency jitter to be amplified, which in turn causes a large angle estimation error problem. 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 a super-helical sliding mode observer, which adopts super-helical sliding mode control to effectively reduce the jitter phenomenon in traditional sliding mode control, so that the permanent magnet synchronous motor in the flywheel system can accurately estimate the position and speed of the rotor during charging and discharging, thereby improving the stability of the system.

[0005] In a first aspect, the present application provides a permanent magnet synchronous motor control method based on a super helical sliding mode observer, comprising:

[0006] A two-phase stationary coordinate system is constructed according to the three-phase stator winding of the permanent magnet synchronous motor, and the stator current and stator voltage in the two-phase stationary coordinate system are obtained;

[0007] According to the sliding mode observer and the super spiral sliding mode control algorithm, the back electromotive force prediction value of the super spiral sliding mode observer is obtained;

[0008] A Gaussian error function is used to smooth the back electromotive force prediction value to weaken the system chattering of the sliding mode control;

[0009] The rotor speed prediction value and the rotor position prediction value of the permanent magnet synchronous motor are obtained according to the back electromotive force prediction value after smoothing.

[0010] From the above, based on the traditional sliding mode observer, this application adopts the super-helical sliding mode control algorithm to construct the super-helical sliding mode observer, and obtains the back-electromotive force prediction value of the super-helical sliding mode observer through convergence calculation, thereby effectively reducing the high-frequency jitter phenomenon in the traditional sliding mode observer. In addition, this application also uses a Gaussian error function to replace the traditional switching function, and smoothes the back-electromotive force prediction value to weaken the system jitter of the sliding mode control. Through this application, the permanent magnet synchronous motor in the flywheel system can accurately estimate the position and speed of the rotor during charging and discharging, thereby improving the stability of the system.

[0011] Optionally, obtaining the back electromotive force prediction value of the super spiral sliding mode observer according to the sliding mode observer and the super spiral sliding mode control algorithm includes:

[0012] According to the stator current, stator voltage and stator resistance in the two-phase stationary coordinate system, the sliding mode observer equation is constructed as follows:

[0013]

[0014] Among them, i α ,i β is the stator current in the two-phase stationary coordinate system, u α ,u β is the stator voltage, R is the stator resistance, L is the inductance in the two-phase stationary coordinate system, is the current prediction value in the two-phase stationary coordinate system, k is the sliding mode gain coefficient;

[0015] According to the sliding mode observer equation and the stator current state equation in the two-phase stationary coordinate system, the sliding mode dynamic error equation is constructed as follows:

[0016]

[0017] in, is the error between the actual value of stator current and the predicted value of current in the two-phase stationary coordinate system, e α ,e β is the back electromotive force in the two-phase stationary coordinate system;

[0018] According to the sliding mode dynamic error equation and the super spiral sliding mode control algorithm, the back electromotive force prediction value of the super spiral sliding mode observer is obtained as follows:

[0019]

[0020] Among them, k1 and k2 are the sliding mode control gain coefficients of the super-helical sliding mode observer, and v1 and v2 are the variable parameters of the super-helical sliding mode control algorithm.

[0021] From the above, in the three-phase surface-mounted PMSM control system, the sliding mode observer is designed by the error between the given current and the feedback current, and the back electromotive force of the motor is reconstructed through the error to estimate the speed and position of the rotor. The equation of the sliding mode observer is constructed according to the current state equation in the two-phase stationary coordinate system, and the sliding mode dynamic error equation can be obtained according to the current state equation and the sliding mode observer equation. Since the traditional sliding mode observer is accompanied by high-frequency jitter, this application uses a super-helical control algorithm to replace the parameters in the equation of the sliding mode observer to obtain the predicted value of the back electromotive force in the super-helical sliding mode observer, thereby effectively reducing the jitter phenomenon in the traditional sliding mode control.

[0022] Optionally, the smoothing process of the back electromotive force prediction value using a Gaussian error function includes:

[0023] A Gaussian error function is used to replace a traditional switching function in the super spiral sliding mode observer to obtain a back electromotive force prediction value of the replaced super spiral sliding mode observer;

[0024] The Gaussian error function is:

[0025]

[0026] The predicted back electromotive force value of the super spiral sliding mode observer after replacement is:

[0027]

[0028] From the above, although the super-helical sliding mode control algorithm can effectively reduce high-frequency jitter phenomenon, the application of traditional switching functions will still cause certain system jitter. Therefore, this application further adopts a Gaussian error function to replace the traditional switching function, thereby smoothing the back-electromotive force prediction value of the super-helical sliding mode observer to weaken the system jitter.

[0029] Optionally, the sliding mode control gain coefficient of the super-helical sliding mode observer adopts an adaptive algorithm to perform adaptive adjustment following the rotor change of the permanent magnet synchronous motor; the adaptive algorithm is:

[0030]

[0031]

[0032] Among them, l1, l2 are control coefficients, ω min is the minimum observed motor speed, ω maxis the maximum value observed for the motor speed, and LPF is a low-pass filter.

[0033] From the above, in the super-helical sliding mode control, if the sliding mode control gain coefficient is selected to be larger, the observation accuracy of the flywheel energy storage system under high-speed operation can be guaranteed, but a larger sliding mode control gain coefficient will cause the performance at low speed to be seriously affected. Therefore, a sliding mode control gain coefficient that follows the rotor adaptive change is proposed. When the speed increases, the sliding mode control gain coefficient begins to increase, thereby increasing the control accuracy of the super-helical sliding mode observer. When the speed decreases, the sliding mode control gain coefficient begins to decrease, which can weaken the system jitter. In this way, the online adaptive adjustment of the super-helical sliding mode observer can more accurately observe the system back electromotive force and increase the accuracy of the observation.

[0034] Optionally, obtaining the predicted value of rotor speed and the predicted value of rotor position of the permanent magnet synchronous motor according to the smoothed predicted value of back electromotive force includes:

[0035] According to the back electromotive force prediction value after smoothing, a phase-locked loop structure is used to extract the rotor speed prediction value and the rotor position prediction value of the permanent magnet synchronous motor.

[0036] From the above, since the traditional inverse tangent estimation method will amplify the interference information in the EMF, the calculation amount is large and the phase compensation is difficult to guarantee, the phase-locked loop (PLL) is used to extract the rotor information, which can effectively improve the rotor tracking speed and obtain accurate rotor speed prediction values ​​and rotor position prediction values.

[0037] In a second aspect, the present application provides a permanent magnet synchronous motor control system based on a super helical sliding mode observer, comprising:

[0038] An acquisition module is used to construct a two-phase stationary coordinate system according to the three-phase stator winding of the permanent magnet synchronous motor, and acquire the stator current and stator voltage in the two-phase stationary coordinate system;

[0039] A super spiral sliding mode observer is used to obtain a back electromotive force prediction value of the super spiral sliding mode observer according to the sliding mode observer and the super spiral sliding mode control algorithm;

[0040] A smoothing processing module, used for smoothing the back electromotive force prediction value by using a Gaussian error function to weaken the system chattering of the sliding mode control;

[0041] The prediction module is used to obtain the rotor speed prediction value and the rotor position prediction value of the permanent magnet synchronous motor according to the back electromotive force prediction value after smoothing.

[0042] In a third aspect, the present application provides a computing device, the computing device comprising:

[0043] processor;

[0044] A memory for storing one or more programs;

[0045] 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 super helical sliding mode observer.

[0046] 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 a super-helical sliding mode observer.

[0047] 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

[0048] Figure 1 A flow chart of a permanent magnet synchronous motor control method based on a super spiral sliding mode observer provided in an embodiment of the present application;

[0049] Figure 2 A schematic diagram of a phase-locked loop structure provided in an embodiment of the present application;

[0050] Figure 3 A schematic diagram of a flywheel energy storage charging and discharging control based on super spiral sliding mode control provided in an embodiment of the present application;

[0051] Figure 4 A structural diagram of a computing device provided in an embodiment of the present application.

[0052] 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

[0053] 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.

[0054] Based on the fact that the existing sliding mode control is accompanied by high-frequency jitter in the sliding mode, the rotor position estimation easily leads to the error of high-frequency jitter being amplified, thereby causing a large angle estimation error problem. The embodiment of the present application provides a permanent magnet synchronous motor control method based on a super-helical sliding mode observer. The method adopts a super-helical sliding mode control algorithm to effectively reduce the jitter phenomenon in the traditional sliding mode control, so that the permanent magnet synchronous motor in the flywheel system can accurately estimate the position and speed of the rotor during charging and discharging, thereby improving the stability of the system.

[0055] like Figure 1 As shown, the embodiment of the present application provides a permanent magnet synchronous motor control method based on a super spiral sliding mode observer, referring to Figure 1 , the method comprising:

[0056] S10: constructing a two-phase stationary coordinate system according to the three-phase stator winding of the permanent magnet synchronous motor, and obtaining a stator current and a stator voltage in the two-phase stationary coordinate system;

[0057] In this step, the permanent magnet synchronous motor includes a three-phase symmetrical winding of the PMSM stator. According to the principle of equivalent fundamental wave synthetic magnetomotive force before and after coordinate transformation, the three-phase stationary coordinate system A, B, C can be transformed into a two-phase stationary coordinate system α, β. The current state equation in the two-phase stationary coordinate system is as follows:

[0058]

[0059]

[0060] Among them, i α ,i β is the stator current in the two-phase stationary coordinate system, u α ,u β is the stator voltage in the two-phase stationary coordinate system, e α ,e β is the back electromotive force in the two-phase stationary coordinate system, R is the stator resistance, L is the inductance in the two-phase stationary coordinate system, ω is the motor speed, ψ is the permanent magnet flux, and θ is the electrical angle.

[0061] S20: obtaining a predicted back electromotive force value of the super spiral sliding mode observer according to the sliding mode observer and the super spiral sliding mode control algorithm;

[0062] In this step, according to the stator current i in the above two-phase stationary coordinate system α ,i β , stator voltage u α ,u β And stator resistance R, the sliding mode observer equation is constructed as:

[0063]

[0064] Among them, i α ,i β is the stator current in the two-phase stationary coordinate system, u α ,u β is the stator voltage, R is the stator resistance, L is the inductance in the two-phase stationary coordinate system, is the current prediction value in the two-phase stationary coordinate system, k is the sliding mode gain coefficient;

[0065] By subtracting the above sliding mode observer equation (3) from the stator current state equation (1) in the two-phase stationary coordinate system, the sliding mode dynamic error equation can be constructed as:

[0066]

[0067] in, is the error between the actual value of stator current and the predicted value of current in the two-phase stationary coordinate system;

[0068] The sliding surface is designed as:

[0069]

[0070] When the sliding mode gain coefficient k is large enough, the sliding mode observer can converge and the sliding mode surface converges to s = 0. At this time, the predicted back electromotive force value is:

[0071]

[0072] In the traditional sliding mode observer control method, the back electromotive force prediction value obtained at this time can be used to obtain the position and speed estimation value of the permanent magnet synchronous motor rotor through the inverse tangent method. However, since the traditional sliding mode observer is accompanied by high-frequency jitter, this embodiment uses the following super-helical control algorithm to replace the parameters in the equation of the sliding mode observer to obtain the back electromotive force prediction value in the super-helical sliding mode observer, thereby effectively reducing the jitter phenomenon in the traditional sliding mode control. Specifically,

[0073] According to the definition of super-helical sliding mode control algorithm:

[0074]

[0075] Substituting the above super-helical sliding mode control algorithm (7) into the above sliding mode dynamic error equation (4), the back electromotive force prediction value of the super-helical sliding mode observer can be obtained as:

[0076]

[0077] Among them, k1 and k2 are the sliding mode control gain coefficients of the super-helical sliding mode observer, and v1 and v2 are the variable parameters of the super-helical sliding mode control algorithm.

[0078] In this step, the parameters in the equation of the sliding mode observer are replaced by using the super-helical control algorithm to obtain the predicted value of the back electromotive force in the super-helical sliding mode observer, thereby effectively reducing the chattering phenomenon in the traditional sliding mode control.

[0079] S30: Smoothing the back electromotive force prediction value using a Gaussian error function to weaken the system chattering of the sliding mode control;

[0080] Although the super-helical sliding mode control algorithm can effectively reduce high-frequency chattering, the application of the traditional switching function will still cause certain system chattering. Therefore, this embodiment further uses a Gaussian error function to replace the traditional switching function, thereby smoothing the back electromotive force prediction value of the super-helical sliding mode observer to weaken the system chattering. Specifically,

[0081] The Gaussian error function is:

[0082]

[0083] Substituting the Gaussian error function (9) into the back electromotive force prediction value (8) of the super spiral sliding mode observer, the back electromotive force prediction value of the super spiral sliding mode observer can be smoothed. The back electromotive force prediction value of the super spiral sliding mode observer after replacement is:

[0084]

[0085] In the super-helical sliding mode control of this embodiment, if the sliding mode control gain coefficient is selected to be larger, the observation accuracy of the flywheel energy storage system under high-speed operation can be guaranteed, but a larger sliding mode control gain coefficient will cause the performance to be seriously affected at low speed. Therefore, a sliding mode control gain coefficient that follows the rotor adaptive change is proposed, as follows:

[0086]

[0087]

[0088] Among them, l1, l2 are control coefficients, ω min is the minimum observed motor speed, ω max is the maximum value observed for the motor speed, and LPF is a low-pass filter.

[0089] Through the above-mentioned adaptive sliding mode control gain coefficient, when the speed increases, the sliding mode control gain coefficient begins to increase, thereby increasing the control accuracy of the super-helical sliding mode observer. When the speed decreases, the sliding mode control gain coefficient begins to decrease, which can weaken the system chattering. In this way, the online adaptive adjustment of the super-helical sliding mode observer can more accurately observe the system back electromotive force and increase the accuracy of the observation.

[0090] S40: Obtaining a rotor speed prediction value and a rotor position prediction value of the permanent magnet synchronous motor according to the smoothed back electromotive force prediction value.

[0091] The traditional inverse tangent estimation method will amplify the interference information in the EMF, and the calculation is large and the phase compensation is difficult to guarantee. Figure 2As shown, this embodiment uses a phase-locked loop (PLL) to extract rotor information, which can effectively improve the rotor tracking speed to obtain accurate rotor speed prediction values ​​and rotor position prediction values.

[0092] Figure 3 A schematic diagram of a flywheel energy storage charging and discharging control based on super spiral sliding mode control provided in an embodiment of the present application, referring to Figure 3 , when the flywheel energy storage system is performing charge and discharge control, Figure 3 The PI2 and PI3 controllers in the embodiment of the present application can use the super-helical sliding mode observer to obtain the back electromotive force prediction value of the super-helical sliding mode observer, and use a phase-locked loop (PLL) to extract the position prediction value and speed prediction value of the rotor, and perform gain control on the rotor according to the predicted value and actual value of the rotor, thereby realizing super-helical sliding mode control during the charging and discharging process of the flywheel energy storage system and providing stability of the flywheel energy storage system.

[0093] In some embodiments, Figure 3 In the schematic diagram of flywheel energy storage charge and discharge control shown in FIG, the PI1 controller can also be designed using a sliding mode controller based on a variable reaching law. Specifically, the permanent magnet synchronous motor is composed 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:

[0094]

[0095] Electromagnetic torque T e The expression is:

[0096]

[0097] 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 in equation (14) is e The expression can be rewritten as:

[0098]

[0099] The motion model of the permanent magnet synchronous motor is:

[0100]

[0101] 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 Ld 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.

[0102] 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:

[0103]

[0104]

[0105] 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;

[0106] An integer-order sliding surface is used, and the sliding surface is defined as:

[0107]

[0108] The traditional exponential approximation rule is:

[0109]

[0110] Based on formula (20), this embodiment designs a new variable convergence law by function replacement:

[0111]

[0112]

[0113] Among them, ε and q are both greater than 0, and 0<δ<1.

[0114] According to the above equations (21) and (22), when the absolute value of s gradually increases, f(s) is approximately When the absolute value of s gradually decreases, f(s) approaches 1. When |s| decreases, the approach speed also decreases. At the same time, the hyperbolic tangent function is used instead of the sign function to weaken the system chattering and improve the system performance. The embodiment of the present application uses the above-mentioned sliding mode controller based on the variable approaching law and the super-helical sliding mode observer to jointly control the permanent magnet synchronous motor control, which can effectively reduce the chattering phenomenon in the traditional sliding mode control, so that the permanent magnet synchronous motor in the flywheel system can accurately estimate the position and speed of the rotor during charging and discharging, thereby improving the stability of the system.

[0115] In summary, the embodiment of the present application provides a permanent magnet synchronous motor control method based on a super-helical sliding mode observer. On the basis of the traditional sliding mode observer, a super-helical sliding mode control algorithm is used to construct a super-helical sliding mode observer, and the back electromotive force prediction value of the super-helical sliding mode observer is obtained through convergence calculation, thereby effectively reducing the high-frequency jitter phenomenon in the traditional sliding mode observer. In addition, the present application also uses a Gaussian error function to replace the traditional switching function, and smoothes the back electromotive force prediction value to weaken the system jitter of the sliding mode control. Through this application, the permanent magnet synchronous motor in the flywheel system can accurately estimate the position and speed of the rotor during charging and discharging, thereby improving the stability of the system.

[0116] Figure 4 1 is a schematic 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 .

[0117] It should be understood that Figure 4 The communication interface 1030 in the computing device 1000 shown may be used to communicate with other devices.

[0118] 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.

[0119] 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 4 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] In the above description, the numbers representing the steps involved do not mean that the steps will be executed accordingly. Intermediate steps may be included or replaced by other steps. The order of the previous and next steps may be interchanged or they may be executed simultaneously if permitted.

[0138] 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.

[0139] 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.

[0140] 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 super helical sliding mode observer, characterized in that: include: A two-phase stationary coordinate system is constructed according to the three-phase stator winding of the permanent magnet synchronous motor, and the stator current and stator voltage in the two-phase stationary coordinate system are obtained; According to the stator current, stator voltage and stator resistance in the two-phase stationary coordinate system, the sliding mode observer equation is constructed as follows: Among them, i α ,i β is the stator current in the two-phase stationary coordinate system, u α ,u β is the stator voltage, R is the stator resistance, L is the inductance in the two-phase stationary coordinate system, is the current prediction value in the two-phase stationary coordinate system, k is the sliding mode gain coefficient; According to the sliding mode observer equation and the stator current state equation in the two-phase stationary coordinate system, the sliding mode dynamic error equation is constructed as follows: in, is the error between the actual value of stator current and the predicted value of current in the two-phase stationary coordinate system, e α ,e β is the back electromotive force in the two-phase stationary coordinate system; According to the sliding mode dynamic error equation and the super spiral sliding mode control algorithm, the back electromotive force prediction value of the super spiral sliding mode observer is obtained as follows: Among them, k1, k2 are the sliding mode control gain coefficients of the super spiral sliding mode observer, v1, v2 are the variable parameters of the super spiral sliding mode control algorithm; The back electromotive force prediction value is smoothed by using a Gaussian error function to weaken the system chattering of the sliding mode control; the back electromotive force prediction value is smoothed by using a Gaussian error function, including: A Gaussian error function is used to replace a traditional switching function in the super spiral sliding mode observer to obtain a back electromotive force prediction value of the replaced super spiral sliding mode observer; The Gaussian error function is: The predicted back electromotive force value of the super spiral sliding mode observer after replacement is: The sliding mode control gain coefficient of the super-helical sliding mode observer adopts an adaptive algorithm and is adaptively adjusted following the rotor changes of the permanent magnet synchronous motor; the adaptive algorithm is: Among them, l1, l2 are control coefficients, ω min is the minimum observed motor speed, ω max is the maximum observed motor speed, LPF is a low-pass filter; The rotor speed prediction value and the rotor position prediction value of the permanent magnet synchronous motor are obtained according to the back electromotive force prediction value after smoothing.

2. The method according to claim 1, characterized in that The step of obtaining the predicted value of rotor speed and the predicted value of rotor position of the permanent magnet synchronous motor according to the predicted value of back electromotive force after smoothing includes: According to the back electromotive force prediction value after smoothing, a phase-locked loop structure is used to extract the rotor speed prediction value and the rotor position prediction value of the permanent magnet synchronous motor.

3. A permanent magnet synchronous motor control system based on a super helical sliding mode observer, characterized in that: include: An acquisition module is used to construct a two-phase stationary coordinate system according to the three-phase stator winding of the permanent magnet synchronous motor, and acquire the stator current and stator voltage in the two-phase stationary coordinate system; The super-helical sliding mode observer is used to construct a sliding mode observer equation according to the stator current, stator voltage and stator resistance in the two-phase stationary coordinate system: Among them, i α ,i β is the stator current in the two-phase stationary coordinate system, u α ,u β is the stator voltage, R is the stator resistance, L is the inductance in the two-phase stationary coordinate system, is the current prediction value in the two-phase stationary coordinate system, k is the sliding mode gain coefficient; According to the sliding mode observer equation and the stator current state equation in the two-phase stationary coordinate system, the sliding mode dynamic error equation is constructed as follows: in, is the error between the actual value of stator current and the predicted value of current in the two-phase stationary coordinate system, e α ,e β is the back electromotive force in the two-phase stationary coordinate system; According to the sliding mode dynamic error equation and the super spiral sliding mode control algorithm, the back electromotive force prediction value of the super spiral sliding mode observer is obtained as follows: Among them, k1, k2 are the sliding mode control gain coefficients of the super spiral sliding mode observer, v1, v2 are the variable parameters of the super spiral sliding mode control algorithm; A smoothing processing module is used to smooth the back electromotive force prediction value by using a Gaussian error function to weaken the system chattering of the sliding mode control; the smoothing processing of the back electromotive force prediction value by using a Gaussian error function includes: A Gaussian error function is used to replace a traditional switching function in the super spiral sliding mode observer to obtain a back electromotive force prediction value of the replaced super spiral sliding mode observer; The Gaussian error function is: The predicted back electromotive force value of the super spiral sliding mode observer after replacement is: The sliding mode control gain coefficient of the super-helical sliding mode observer adopts an adaptive algorithm and is adaptively adjusted following the rotor changes of the permanent magnet synchronous motor; the adaptive algorithm is: Among them, l1, l2 are control coefficients, ω min is the minimum observed motor speed, ω max is the maximum observed motor speed, LPF is a low-pass filter; The prediction module is used to obtain the rotor speed prediction value and the rotor position prediction value of the permanent magnet synchronous motor according to the back electromotive force prediction value after smoothing.

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 a super helical sliding mode observer 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 the super helical sliding mode observer as described in any one of claims 1 to 2 is implemented.

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