A permanent magnet synchronous linear motor thrust fluctuation suppression method, electronic equipment and storage medium
Patent Information
- Application Number
- CN202611064884.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-07-17
- Publication Date
- 2026-10-27
AI Technical Summary
[0004]针对现有技术中传统自抗扰控制在直线电机电流环中难以无静差提取和补偿高频周期性电气扰动，从而导致推力波动的问题，本申请提供了一种基于迭代锁相环观测器的自抗扰控制方法，旨在从电流环底层实现永磁同步直线电机因逆变器非线性和反电动势谐波等引起的推力波动的高精度抑制
1、本申请所提出的迭代锁相环观测器继承了锁相环观测器对斜坡扰动的无静差跟踪优点，其干扰估计误差传递函数的分子相比传统扩张状态观测器，对相同频率的低频扰动具有更小的动态跟踪误差，能够实现对斜坡扰动的无静差跟踪。
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Abstract
Description
Technical Field
[0001] This application relates to the field of motor control technology, and in particular to a method for suppressing thrust fluctuations in a permanent magnet synchronous linear motor, an electronic device, and a storage medium. Background Technology
[0002] Permanent magnet synchronous linear motors (PMSMs), by eliminating the traditional intermediate mechanical transmission links and achieving direct drive from electrical energy to linear motion, offer significant advantages such as fast response speed, high positioning accuracy, high thrust density, and reliable operation. They are widely used in high-end equipment fields such as semiconductor manufacturing, high-end CNC machine tools, and precision micro-motion platforms. However, due to the non-ideal characteristics of the motor's physical structure and the nonlinear factors of the drive system, PMSMs inevitably experience thrust fluctuations during actual operation. Specifically, the back electromotive force harmonics caused by the distortion of the permanent magnet's magnetic field, along with the inverter dead-time effect and switching transistor voltage drop nonlinearity commonly found in the drive system, lead to the introduction of a large number of periodic harmonic components strongly correlated with the fundamental frequency into the motor's stator current. Since the motor's electromagnetic thrust is directly related to the stator current, these high-frequency alternating distorted currents directly translate into complex periodic thrust fluctuations. These thrust fluctuations weaken the system's steady-state accuracy, which has become a key technical bottleneck restricting the further promotion of linear motors in the field of ultra-precision servo systems.
[0003] To suppress various electrical disturbances and improve the current tracking performance of the drive system, Active Disturbance Rejection Control (ADRC), as an advanced robust control strategy, has been introduced into the current loop control of linear motors. ADRC does not rely on a precise mathematical model of the controlled object; its core lies in using an extended state observer to treat internal parameter perturbations and external unmodeled dynamics as a total disturbance for real-time observation and active compensation. While traditional ADRC exhibits excellent robustness in dealing with gradually changing or step-like parameter disturbances, it reveals significant limitations when addressing high-frequency thrust fluctuations caused by electrical harmonics. Back-EMF harmonics and disturbances caused by inverter nonlinearity are high-frequency, periodic alternating signals. Traditional current loop extended state observers, limited by their bandwidth, experience severe phase lag and amplitude attenuation when extracting such high-frequency periodic disturbances, making it difficult to achieve accurate observation and complete compensation without steady-state error. This results in residual harmonic currents remaining, preventing the complete elimination of thrust fluctuations from the underlying drive end. Therefore, in view of the shortcomings of traditional active disturbance rejection control in dealing with high-frequency periodic electrical disturbances, it is urgent to construct an active disturbance rejection control method with periodic disturbance suppression capability, in order to achieve efficient and accurate compensation for unknown frequency harmonic disturbances in the current loop, and effectively suppress the thrust fluctuation of permanent magnet synchronous linear motor from the source. Summary of the Invention
[0004] To address the problem that traditional active disturbance rejection control (ADRC) in the current loop of linear motors struggles to extract and compensate for high-frequency periodic electrical disturbances without steady-state error, thus causing thrust fluctuations, this application provides an ADRC method based on an iterative phase-locked loop observer. This method aims to achieve high-precision suppression of thrust fluctuations in permanent magnet synchronous linear motors caused by inverter nonlinearity and back EMF harmonics from the bottom layer of the current loop.
[0005] To achieve the above objectives, this application provides a method for suppressing thrust fluctuations in a permanent magnet synchronous linear motor, comprising the following steps: S1: Obtain the reference currents of the d-axis and q-axis of the permanent magnet synchronous linear motor, as well as the actual currents of the d-axis and q-axis; S2: Run an iteratively learned phase-locked loop observer to estimate the lumped perturbation along the d-axis, the estimation steps including: The d-axis lumped disturbance estimate is decomposed into a d-axis periodic disturbance estimate and a d-axis non-periodic disturbance estimate. Based on the d-axis current estimation error between the actual d-axis current and a d-axis current estimate maintained inside the iterative learning phase-locked loop observer, the portion of the d-axis aperiodic perturbation estimate is updated. An iterative learning approach is adopted, based on the estimated value of the d-axis periodic disturbance in the previous iteration and the estimation error of the d-axis current in the current iteration, to update the estimated value of the d-axis periodic disturbance in the current iteration. S3: Based on the d-axis reference current, the d-axis actual current, and the d-axis lumped disturbance estimate synthesized from the d-axis periodic disturbance estimate and the d-axis non-periodic disturbance estimate, generate the final d-axis voltage control command; S4: Output the final d-axis voltage control command and a final q-axis voltage control command to drive the permanent magnet synchronous linear motor.
[0006] Furthermore, the method further includes: performing an operation corresponding to the d-axis on the q-axis to estimate the lumped disturbance of the q-axis, and generating the final q-axis voltage control command based on the estimated q-axis lumped disturbance.
[0007] Furthermore, the step of generating the final d-axis voltage control command in step S3 includes: The difference between the d-axis reference current and the d-axis actual current is calculated to obtain the d-axis current error; The final d-axis voltage control command is calculated based on a feedback control law that includes the differential term of the d-axis reference current, the proportional term of the d-axis current error, and the estimated value of the d-axis lumped disturbance.
[0008] Furthermore, the iteration period of the iterative learning method is determined based on the permanent magnet pole pitch and mover speed of the permanent magnet synchronous linear motor.
[0009] Furthermore, the iterative learning method also includes applying a low-pass filter to the update amount calculated based on the d-axis current estimation error.
[0010] Furthermore, the low-pass filter used in the low-pass filtering process has a filter coefficient that is a positive number less than 1.
[0011] Furthermore, the iterative learning method also includes applying a forgetting factor to attenuate the estimated value of the d-axis periodic perturbation in the previous iteration period.
[0012] Furthermore, in step S2, the observer gain used to update the estimated value of the d-axis aperiodic disturbance is set based on a preset observer bandwidth.
[0013] This application also provides an electronic device, including: a memory and a processor coupled to each other, the processor being configured to execute program instructions stored in the memory to implement the thrust fluctuation suppression method for a permanent magnet synchronous linear motor as described in any of the preceding claims.
[0014] This application also provides a computer-readable storage medium storing program data that can be executed by a processor to implement the thrust fluctuation suppression method for a permanent magnet synchronous linear motor as described in any of the preceding claims.
[0015] Due to the adoption of the above technical solution, this application has at least one of the following beneficial effects compared with the prior art: 1. The iterative phase-locked loop observer proposed in this application inherits the advantage of zero steady-state error tracking of the phase-locked loop observer for slope disturbances. Compared with the traditional extended state observer, the numerator of its disturbance estimation error transfer function has a smaller dynamic tracking error for low-frequency disturbances of the same frequency, and can achieve zero steady-state error tracking of slope disturbances.
[0016] 2. This application enhances the observer's ability to capture periodic disturbances by introducing a proportional iterative learning stage with a forgetting factor into the disturbance estimation term of the phase-locked loop observer. It can achieve significant amplitude attenuation of periodic disturbances at specific frequencies, thereby effectively suppressing high-frequency periodic thrust fluctuations caused by back EMF harmonics and inverter nonlinearity.
[0017] 3. This application simultaneously feeds forward to compensate for the periodic and non-periodic disturbances observed by the iterative phase-locked loop observer, effectively suppressing the thrust fluctuation of the permanent magnet synchronous linear motor from the bottom layer of the current loop, and significantly improving both thrust fluctuation and current distortion. Attached Figure Description
[0018] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein: Figure 1 A control block diagram of a permanent magnet synchronous linear motor based on iterative phase-locked loop self-disturbance rejection provided in an embodiment of this application; Figure 2 A block diagram of an active disturbance rejection control structure for a permanent magnet synchronous linear motor based on an iterative phase-locked loop observer provided in an embodiment of this application; Figure 3 A flowchart illustrating a method for suppressing thrust fluctuations in a permanent magnet synchronous linear motor according to an embodiment of this application; Figure 4 This is a Bode diagram illustrating the interference estimation error for different observers according to an embodiment of this application. Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application; Figure 6 This is a schematic diagram of the structure of a computer-readable storage medium provided in an embodiment of this application; Figure 7 The thrust curve and three-phase current curve of traditional active disturbance rejection control and their Fourier analysis plots are shown. Figure 8 The thrust curve and three-phase current curve of the phase-locked loop active disturbance rejection control and their Fourier analysis plots are shown. Figure 9 The thrust curves and three-phase current curves of the iterative phase-locked loop active disturbance rejection control, along with their Fourier analysis plots, are presented. Detailed Implementation
[0019] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It is understood that the specific embodiments described herein are only for explaining this application and not for limiting it. Furthermore, it should be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all structures. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.
[0020] The terms "first," "second," etc., used in this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.
[0021] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.
[0022] like Figure 1 As shown, Figure 1 This is a block diagram of a permanent magnet synchronous linear motor (PMSM) control system based on an iterative phase-locked loop (PLL) active disturbance rejection (ADNR) according to an embodiment of this application. The entire control system includes: an iterative PLL ADNR controller (containing an iterative PLL observer and a linear feedback control law), a coordinate transformation module, an SVPWM module, an inverter, a PMSM, and a current sampling module. Here, PI represents a proportional-integral controller, and SVPWM represents space vector pulse width modulation. The control system inputs the given d-axis and q-axis reference currents and the actually acquired d-axis and q-axis currents into the iterative PLL observer, which outputs a lumped disturbance estimate. The linear feedback control law generates a voltage control command based on the reference current, the actual current, and the disturbance estimate. The voltage command, after coordinate transformation and SVPWM modulation, drives the inverter, thereby controlling the operation of the PMSM.
[0023] like Figure 2 As shown, Figure 2 This is a block diagram of an active disturbance rejection control structure for a permanent magnet synchronous linear motor based on an iterative phase-locked loop (PLL) observer, provided in one embodiment of this application. The iterative PLL observer includes a current estimation stage and a disturbance estimation stage. The disturbance estimation stage is further divided into aperiodic disturbance estimation and periodic disturbance estimation. The periodic disturbance estimation adopts an iterative learning method, updating the value using the periodic disturbance estimate from the previous iteration and the current estimation error of the current cycle, and then processing it with low-pass filtering and a forgetting factor.
[0024] like Figure 3 As shown, Figure 3 A flowchart illustrating a method for suppressing thrust fluctuations in a permanent magnet synchronous linear motor according to an embodiment of this application includes the following steps: S1: Obtain the d-axis and q-axis reference currents, as well as the actual d-axis and q-axis currents of the permanent magnet synchronous linear motor. Specifically, the three-phase stator current of the permanent magnet synchronous linear motor is collected by a current sensor, and the actual d-axis and q-axis currents are obtained after Clark transformation and Park transformation. The d-axis reference current and q-axis reference current are given by the host computer or speed loop controller. In this embodiment, the current sampling frequency is 10kHz, which matches the controller interrupt cycle. In other embodiments, the current sampling frequency can be selected separately, and no limitation is made on this.
[0025] S2: Run an iteratively learned phase-locked loop observer to estimate the lumped perturbation along the d-axis. The estimation steps include: The d-axis lumped disturbance estimate is decomposed into a d-axis periodic disturbance estimate and a d-axis aperiodic disturbance estimate. The d-axis aperiodic disturbance estimate is updated based on the d-axis current estimation error between the actual d-axis current and a d-axis current estimate maintained within the iterative learning PLL observer. Using an iterative learning approach, the d-axis periodic disturbance estimate is updated based on the d-axis periodic disturbance estimate from the previous iteration and the d-axis current estimation error from the current iteration.
[0026] First, a mathematical model of the current loop of the permanent magnet synchronous linear motor, including lumped disturbances, is constructed. The mathematical model of the current loop of the permanent magnet synchronous linear motor is expressed as follows (1):
[0027] in, and Represent shaft and shaft current, and Represent shaft and Voltage of the shaft, Represents the primary winding resistance. and Represent shaft and The primary winding inductance of the shaft, It is a permanent magnet flux chain. It is the motioner velocity. It is the pole pitch of the permanent magnet; the thrust fluctuation suppression method is applied to the permanent magnet synchronous linear motor, then .
[0028] In actual permanent magnet synchronous linear motor systems, disturbances such as motor parameter perturbations, inverter nonlinearity, and back EMF harmonics will occur. If these are regarded as lumped disturbances, the model can be further expressed as the following equation (2):
[0029] In the formula, The nominal value representing the inductance. and Represent shaft and The lumped disturbance of the axis is specifically expressed as follows (3):
[0030] In the formula, and The nominal values representing resistance and magnetic flux linkage. This is the resistance perturbation value. This is the flux perturbation value. This is the inductance perturbation value. and They are respectively shaft and Periodic disturbances of the axis.
[0031] According to equation (2), The mathematical models for axes are the same, therefore... Taking the shaft model as an example, we will design the subsequent observer and controller. The same steps are used for the shaft, so they will not be repeated here. Shaft design process. (Let) , , , ,but The iterative learning phase-locked loop observer for the axis is designed as follows (4):
[0032] in, , and They are , and The observed values, Representing a time series, Indicates the iteration period. For low-pass filters, a positive number less than 1 is usually chosen. Forgetting factor, For learning gain, and This is the observer gain.
[0033] The iteration period of the iterative learning method is determined based on the permanent magnet pole pitch and mover velocity of the permanent magnet synchronous linear motor; in this embodiment, the iteration period is... .
[0034] The iterative learning method also includes applying low-pass filtering to the update calculated based on the d-axis current estimation error; the low-pass filter used in the low-pass filtering process has a filter coefficient that is a positive number less than 1.
[0035] In this embodiment, For a low-pass filter, a positive number less than 1 is selected, preferably. .
[0036] The iterative learning method also includes applying a forgetting factor to attenuate the estimated value of the d-axis periodic perturbation from the previous iteration; in this embodiment, Forgetting factor; preferably, The forgetting factor is equivalent to applying an exponentially decaying weight in the time dimension, gradually forgetting old data and effectively suppressing noise; in addition, the forgetting factor improves the phase margin, which is beneficial to system stability.
[0037] The observer gain is used to update the d-axis aperiodic perturbation estimate; the observer gain is set based on a preset observer bandwidth. and Based on the bandwidth method, it is usually set as follows (5):
[0038] In the formula, This indicates the observer bandwidth.
[0039] To illustrate the advantages of the iterative learning phase-locked loop observer in this application, the disturbance estimation transfer function of the traditional observer is derived. and the error transfer function of disturbance estimation As shown in equation (6):
[0040] The disturbance estimation transfer function of the phase-locked loop observer is derived. and the error transfer function of disturbance estimation As shown in equation (7):
[0041] The disturbance estimation transfer function of the iterative phase-locked loop observer is derived. and the error transfer function of disturbance estimation As shown in equation (8):
[0042] As can be seen from equations (6), (7), and (8), at least one of the numerators of the disturbance estimation error transfer function of the three observers is present. This means that all three observers can eliminate steady-state errors caused by constant disturbances; the numerator of the error transfer function of the phase-locked loop observer is This means that, theoretically, it can achieve zero steady-state error tracking of slope disturbances, which is something that traditional extended state observers cannot do. The iterative phase-locked loop observer in this application inherits the advantage of zero steady-state error tracking of slope disturbances from the phase-locked loop observer, and further enhances the ability to capture periodic disturbances through iterative learning of the denominator.
[0043] Substituting equation (5) into equations (6), (7), and (8), let... ,but , And make , , , Based on the interference estimation error transfer function of the three observers, the corresponding Bode plot is drawn; such as Figure 4 As shown, Figure 4 This is a Byrd diagram illustrating the interference estimation errors of different observers provided in an embodiment of this application. As shown in the figure, the error curve of the traditional extended state observer rises with a slope of +20 dB / dec in the low-frequency range, indicating that its ability to suppress disturbances is limited. Both the phase-locked loop (PLL) observer and the iterative PLL observer rise with a slope of +40 dB / dec in the low-frequency range, and their curve positions are significantly lower than those of the traditional extended state observer. This means that for low-frequency disturbances of the same frequency, both have smaller dynamic tracking errors. Furthermore, it can be seen that the iterative PLL observer exhibits significant amplitude attenuation at a specific set frequency, indicating extremely high suppression capability for periodic disturbances at that frequency.
[0044] S3: Based on the d-axis reference current, the actual d-axis current, and the d-axis lumped disturbance estimate synthesized from the d-axis periodic disturbance estimate and the d-axis non-periodic disturbance estimate, generate the final d-axis voltage control command.
[0045] Step S3, which generates the final d-axis voltage control command, includes: Calculate the difference between the d-axis reference current and the actual d-axis current to obtain the d-axis current error; The final d-axis voltage control command is calculated based on a feedback control law that includes the differential term of the d-axis reference current, the proportional term of the d-axis current error, and the estimated value of the d-axis lumped disturbance.
[0046] definition Shaft reference current is ,make ,but .
[0047] Based on the feedback control law, we can obtain the following equation (10):
[0048] In the formula, yes d Shaft current controls gain. Substituting equation (10) into equation (9), we obtain equation (11):
[0049] Based on the estimation information of non-periodic and periodic disturbances obtained from equation (4), and substituted into equation (11), the active disturbance rejection control law is rewritten as equation (12):
[0050] S4: Output a final d-axis voltage control command and a final q-axis voltage control command to drive a permanent magnet synchronous linear motor; the method further includes: performing an operation corresponding to the d-axis on the q-axis to estimate the lumped disturbance of the q-axis, and generating a final q-axis voltage control command based on the lumped disturbance estimate.
[0051] Regarding the above embodiments, this application provides an electronic device; please refer to [link / reference]. Figure 5 , Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The computer device includes a memory and a processor, wherein the memory and the processor are coupled to each other. The memory stores program data, and the processor is used to execute the program data to implement the steps of any embodiment of the above-described method for suppressing thrust fluctuations in a permanent magnet synchronous linear motor.
[0052] In this embodiment, the processor may also be referred to as a CPU (Central Processing Unit). The processor may be an integrated circuit chip with signal processing capabilities. The processor may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. A general-purpose processor may be a microprocessor or any conventional processor.
[0053] The methods described in the above embodiments can be implemented as computer programs; therefore, this application proposes a computer-readable storage medium. Please refer to [link to relevant documentation]. Figure 6 , Figure 6 This is a schematic diagram of a computer-readable storage medium provided in one embodiment of this application. The computer-readable storage medium stores program data that can be executed by a processor to implement the steps of any embodiment of the above-described method for suppressing thrust fluctuations in a permanent magnet synchronous linear motor.
[0054] In this embodiment, the computer-readable storage medium can be a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, or a medium capable of storing program data. Alternatively, it can be a server storing the program data, which can send the stored program data to other devices for execution or run the stored program data itself.
[0055] To verify the effectiveness of the iterative phase-locked loop (PLL) active disturbance rejection controller (ADRC) in a permanent magnet synchronous linear motor (PMSM), simulations were performed on traditional ADRC, PLL ADRC, and the proposed iterative PLL ADRC under the same control and observation bandwidths. The specific parameters of the PMSM are as follows: m, H, =0.17Ω, Wb, mover mass 0.9kg. Current loop active disturbance rejection controller gain. Set to 1621.2. Set to 131.9, and set the parameters related to iterative learning as follows: , , If the reference speed of the permanent magnet synchronous linear motor is set to 2 m / s, then... According to The calculation shows that the inverter dead time is set to 5% of the switching cycle.
[0056] Figure 7 The thrust curve and three-phase current curve of traditional active disturbance rejection control and their Fourier analysis plots are shown. Figure 8 The thrust curve and three-phase current curve of the phase-locked loop active disturbance rejection control and their Fourier analysis plots are shown. Figure 9 The thrust curves and three-phase current curves of the iterative phase-locked loop active disturbance rejection control, along with their Fourier analysis plots, are presented.
[0057] from Figure 7 As shown in Figure 9, traditional active disturbance rejection control (ADRC) exhibits large electromagnetic thrust fluctuations (THD 3.24%) and significant three-phase current distortion (THD 8.27%). Phase-locked loop (PLL) ADRC slightly reduces electromagnetic thrust fluctuations (THD 2.97%), but the three-phase current waveform remains largely unchanged (THD 8.23%). The proposed iterative PLL ADRC significantly reduces electromagnetic thrust fluctuations (THD 1.51%) and significantly improves the sinusoidal nature of the three-phase current (THD 3.10%). Therefore, the iterative PLL ADRC proposed in this application effectively suppresses thrust fluctuations in permanent magnet synchronous linear motors.
[0058] In the several embodiments provided in this application, it should be understood that the disclosed methods and devices can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.
[0059] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.
[0060] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.
[0061] The above description is merely an embodiment of this application and does not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.
Claims
1. A method for suppressing thrust fluctuations in a permanent magnet synchronous linear motor, characterized in that, Includes the following steps: S1: Obtain the reference currents of the d-axis and q-axis of the permanent magnet synchronous linear motor, as well as the actual currents of the d-axis and q-axis; S2: Run an iteratively learned phase-locked loop observer to estimate the lumped perturbation along the d-axis, the estimation steps including: The d-axis lumped disturbance estimate is decomposed into a d-axis periodic disturbance estimate and a d-axis aperiodic disturbance estimate. Based on the d-axis current estimation error between the actual d-axis current and a d-axis current estimate maintained within the iterative learning PLL observer, the d-axis aperiodic disturbance estimate is updated. Using an iterative learning approach, the d-axis periodic disturbance estimate for the current period is updated based on the d-axis periodic disturbance estimate from the previous iteration and the d-axis current estimation error for the current period. S3: Based on the d-axis reference current, the d-axis actual current, and the d-axis lumped disturbance estimate synthesized from the d-axis periodic disturbance estimate and the d-axis non-periodic disturbance estimate, generate the final d-axis voltage control command; S4: Output the final d-axis voltage control command and a final q-axis voltage control command to drive the permanent magnet synchronous linear motor.
2. The method according to claim 1, characterized in that, The method further includes: performing an operation corresponding to the d-axis on the q-axis to estimate the lumped disturbance of the q-axis, and generating the final q-axis voltage control command based on the estimated q-axis lumped disturbance.
3. The method according to claim 1, characterized in that, The step of generating the final d-axis voltage control command in step S3 includes: The difference between the d-axis reference current and the d-axis actual current is calculated to obtain the d-axis current error; The final d-axis voltage control command is calculated based on a feedback control law that includes the differential term of the d-axis reference current, the proportional term of the d-axis current error, and the estimated value of the d-axis lumped disturbance.
4. The method according to claim 1, characterized in that, The iteration period of the iterative learning method is determined based on the permanent magnet pole pitch and mover speed of the permanent magnet synchronous linear motor.
5. The method according to claim 1, characterized in that, The iterative learning method also includes applying a low-pass filter to the update amount calculated based on the d-axis current estimation error.
6. The method according to claim 5, characterized in that, The low-pass filter used in the low-pass filtering process has a filter coefficient that is a positive number less than 1.
7. The method according to claim 1, characterized in that, The iterative learning method further includes applying a forgetting factor to attenuate the estimated value of the d-axis periodic perturbation in the previous iteration cycle.
8. The method according to claim 1, characterized in that, In step S2, the observer gain is used to update the portion of the d-axis aperiodic disturbance estimate, and the observer gain is set based on a preset observer bandwidth.
9. An electronic device, characterized in that, The electronic device includes a memory and a processor coupled to each other, the processor being used to execute program instructions stored in the memory to implement the thrust fluctuation suppression method for a permanent magnet synchronous linear motor as described in any one of claims 1-8.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores program data that can be executed by a processor to implement the thrust fluctuation suppression method for a permanent magnet synchronous linear motor as described in any one of claims 1-8.