A control method and related device of a permanent magnet synchronous motor based on disturbance correlation separation and dynamic stability switching

Abstract

The application discloses a permanent magnet synchronous motor control method and related device based on disturbance correlation separation and dynamic steady switching, relates to the technical field of motor servo control, and comprises the following steps: collecting real-time running state data of a permanent magnet synchronous motor, and acquiring an observed value of a lumped disturbance of the system through a nonlinear extended state observer; based on a time sequence signal correlation suppression mechanism, updating an equivalent viscous friction coefficient according to the observed value and the state data, and extracting a pure external high-frequency mechanical load; substituting the updated equivalent viscous friction coefficient and the pure external high-frequency mechanical load into a dynamic twin module to obtain a full-band high-fidelity prediction model; based on the prediction model, parallelly calculating a transient current instruction of a model prediction control mode and a steady matching current instruction of a digital twin direct control mode; based on a smooth switching discriminant function, performing integral-free switching between the transient current instruction and the steady matching current instruction; and outputting the determined control instruction to a bottom-level inverter.

Description

Technical Field

[0001] This application relates to the field of motor servo control technology, and in particular to a control method and related device for a permanent magnet synchronous motor (PMSM) based on disturbance correlation separation and dynamic steady-state switching. Background Technology

[0002] Permanent magnet synchronous motors (PMSMs), with their high torque-to-inertia ratio, high power density, and excellent operating efficiency, have been widely used as core power units in modern manufacturing industries such as aerospace electric propulsion, precision CNC machine tools, and high-end industrial robots. As industry demands increasingly higher precision in trajectory tracking and robustness of motors under complex and variable operating conditions, Model Predictive Control (MPC), with its intuitive rolling optimization mechanism, extremely fast dynamic response, and ease of handling nonlinear constraints, has become an important research direction in the field of high-performance servo control of motors.

[0003] However, the large-scale engineering application of high-performance MPC algorithms in underlying high-frequency hardware drivers still faces two major theoretical and engineering challenges: 1) Strong coupling of multi-source disturbances in the physical system leads to severe mismatch in the prediction model. This results in lumped disturbance observations often carrying multiplicative coupling errors that are strongly correlated with the motor state during dynamic processes such as motor acceleration / deceleration or drastic load changes.

[0004] 2) The computational resources of the underlying Digital Signal Processor (DSP) are extremely limited, leading to steady-state high-frequency chattering. This steady-state chattering not only degrades the surface quality of CNC machine tools but also significantly reduces the service life of the mechanical transmission chain.

[0005] Currently, although Digital Twin (DT) technology has begun to be applied to motor condition monitoring and fault diagnosis, how to deeply integrate DT's dynamic high-fidelity modeling capabilities with MPC's rolling optimization mechanism to build a new control architecture that can accurately decouple physical disturbances, take into account both transient ultra-fast response and steady-state chatter-free operation, and significantly reduce the consumption of underlying computing power remains a technical challenge that needs to be solved in this field. Summary of the Invention

[0006] The purpose of this application is to provide a control method and related device for permanent magnet synchronous motor based on disturbance correlation separation and dynamic steady-state switching. This method solves the problem of black-box coupling of physical disturbance sources, realizes the precise separation of internal motor parameter drift from external load, significantly reduces the computing power consumption during steady-state operation, and improves the dynamic response and steady-state stability of the system.

[0007] To achieve the above objectives, this application provides the following solution: In a first aspect, this application provides a control method for a permanent magnet synchronous motor based on disturbance correlation separation and dynamic steady-state switching, including: The system collects real-time operating status data of the permanent magnet synchronous motor and obtains the lumped disturbance observation value of the system through a nonlinear extended state observer; the system is a permanent magnet synchronous motor control system based on disturbance correlation separation and dynamic steady-state switching. Based on the time-series signal correlation suppression mechanism, the equivalent viscous friction coefficient is updated according to the lumped disturbance observation value and real-time operating status data, and the pure external high-frequency mechanical load is extracted. Substituting the updated equivalent viscous friction coefficient and the pure external high-frequency mechanical load into the dynamic twin module, we obtain a full-band high-fidelity prediction model. Based on the full-band high-fidelity prediction model, the parallel computing model predicts the transient current command of the control mode and the steady-state matching current command of the digital twin direct control mode. Based on the smooth switching discrimination function, a non-integral switching is performed between the transient current command and the steady-state matching current command, and the determined control command is output to the underlying inverter.

[0008] In a second aspect, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the permanent magnet synchronous motor control method based on disturbance correlation separation and dynamic steady-state switching as described above.

[0009] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the permanent magnet synchronous motor control method based on disturbance correlation separation and dynamic steady-state switching as described above.

[0010] Fourthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the permanent magnet synchronous motor control method based on disturbance correlation separation and dynamic steady-state switching as described above.

[0011] According to the specific embodiments provided in this application, this application has the following technical effects: This application provides a control method and related device for permanent magnet synchronous motors based on disturbance correlation separation and dynamic-steady-state switching. Utilizing a time-series signal correlation suppression mechanism, it isolates the equivalent viscous friction coefficient strongly correlated with rotational speed and the independent pure external high-frequency mechanical load from the lumped disturbance obtained from the nonlinear extended state observer. This solves the problem of prediction model mismatch caused by multi-source disturbance coupling. By constructing a dynamic twin module, a full-band high-fidelity prediction model is obtained, providing an accurate model foundation for subsequent control command calculation. Furthermore, by parallel computing of current commands for two control modes and combining a smooth switching discriminant function, disturbance-free switching between dynamic and steady-state stages is achieved. In the transient stage, the advantages of rapid response from model predictive control are leveraged, while in the steady-state stage, it switches to digital twin direct control with lower computational load. This retains excellent dynamic tracking performance while eliminating steady-state high-frequency chattering, significantly reducing the computational power requirements of the underlying hardware processor. It balances control performance and engineering feasibility, meeting the needs of high-precision servo control of permanent magnet synchronous motors under complex operating conditions. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in 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.

[0013] Figure 1 This is an application environment diagram of a permanent magnet synchronous motor control method based on disturbance correlation separation and dynamic steady-state switching in one embodiment of this application; Figure 2 A flowchart illustrating a permanent magnet synchronous motor control method based on disturbance correlation separation and dynamic steady-state switching, provided as an embodiment of this application; Figure 3 This is a block diagram of the overall architecture of a control system based on disturbance correlation separation and dynamic steady-state switching in one embodiment of this application; Figure 4 This is a diagram showing the internal structure and signal flow of a timing signal correlation separation and observation module in one embodiment of this application; Figure 5 This is a flowchart illustrating the logic judgment and execution of dual-modal non-integral smooth switching in one embodiment of this application; Figure 6 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application.

[0014] Figure label: 102 - Terminal, 104 - Server. Detailed Implementation

[0015] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0016] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0017] The permanent magnet synchronous motor control method based on disturbance correlation separation and dynamic steady-state switching provided in this application can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be set up independently, integrated into server 104, or placed in the cloud or on another server. Terminal 102 can send lumped disturbance observations and real-time operating status data to server 104. Upon receiving the lumped disturbance observations and real-time operating status data, server 104 updates the equivalent viscous friction coefficient and extracts the pure external high-frequency mechanical load based on a time-series signal correlation suppression mechanism. The updated equivalent viscous friction coefficient and the pure external high-frequency mechanical load are then substituted into the dynamic twin module to obtain a full-band high-fidelity prediction model. Based on this model, the transient current command of the predicted control mode and the steady-state matching current command of the digital twin direct control mode are calculated in parallel. Using a smooth switching discrimination function, a non-integral switching is performed between the transient current command and the steady-state matching current command, and the determined control command is output to the underlying inverter. Server 104 can then feed back the obtained control command to terminal 102. Furthermore, in some embodiments, the permanent magnet synchronous motor control method based on disturbance correlation separation and dynamic steady-state switching can also be implemented separately by the server 104 or the terminal 102. For example, the terminal 102 can directly perform permanent magnet synchronous motor control based on disturbance correlation separation and dynamic steady-state switching on the lumped disturbance observations and real-time operating status data. Alternatively, the server 104 can obtain the lumped disturbance observations and real-time operating status data from the data storage system and perform permanent magnet synchronous motor control based on disturbance correlation separation and dynamic steady-state switching on the lumped disturbance observations and real-time operating status data.

[0018] The terminal 102 can be, but is not limited to, various desktop computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, and smart in-vehicle devices. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted devices. The server 104 can be implemented using a standalone server or a server cluster composed of multiple servers, or it can be a cloud server.

[0019] In one exemplary embodiment, such as Figure 2 As shown, a control method for a permanent magnet synchronous motor based on disturbance correlation separation and dynamic steady-state switching is provided. This method is executed by a computer device, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is applied to... Figure 1 Taking server 104 as an example, the explanation includes the following steps 201 to 205. Wherein: Step 201: Collect real-time operating status data of the permanent magnet synchronous motor and obtain the lumped disturbance observation value of the system through a nonlinear extended state observer; the system is a current closed-loop control system for the permanent magnet synchronous motor. Step 202: Based on the time-series signal correlation suppression mechanism, update the equivalent viscous friction coefficient according to the lumped disturbance observation value and real-time operating status data, and extract the pure external high-frequency mechanical load; Step 203: Substitute the updated equivalent viscous friction coefficient and the pure external high-frequency mechanical load into the dynamic twin module to obtain a full-band high-fidelity prediction model. Step 204: Based on the full-band high-fidelity prediction model, the model is used to predict the transient current command of the control mode and the steady-state matching current command of the digital twin direct control mode in parallel computing mode. Step 205: Based on the smooth switching discrimination function, perform non-integral switching between the transient current command and the steady-state matching current command, and output the determined control command to the underlying inverter.

[0020] This embodiment provides a control method for permanent magnet synchronous motors based on disturbance correlation separation and dynamic-steady-state switching. In actual operation, the physical motor's parameter drift (such as friction coefficient temperature rise changes) is deeply coupled with external random loads. Directly using a traditional Extended State Observer (ESO) to treat this as a "black box" lumped disturbance for observation and compensation cannot solve the multiplicative state coupling error, leading to prediction model mismatch and steady-state chattering. Therefore, this invention introduces a decoupling method based on time-series signal correlation suppression, orthogonally separating internal parameter drift from external high-frequency loads at the physical mechanism level, and then employing a non-integral dual-modal control architecture for smooth dynamic-steady-state drive.

[0021] Taking an intelligent electric drive platform (which includes a surface-mounted permanent magnet synchronous motor (SPMSM), power inverter, sensors, and a low-level digital signal processor (DSP)) as an example, such as... Figure 5 As shown, the closed-loop control algorithm enters a loop iteration after initial parameter configuration. The system first samples the actual motor state and calculates the tracking error. Then, it uses a Discrete Extended State Observer (D-ESO) to estimate the lumped disturbance, updates the equivalent damping in real time through a decoupling mechanism, and extracts the pure external high-frequency load. Next, the corrected parameters and load are injected into the twin prediction model to achieve dynamic reconstruction of the high-fidelity model. Based on this model, dual-mode candidate current commands for transient model predictive control (MPC) and steady-state digital twin direct control (DTDC) are calculated in parallel. Finally, the system calculates a smooth switching discriminant function and compares it with a preset threshold to intelligently determine whether the motor is currently in transient or steady state. It then selects to execute the corresponding MPC or DTDC command and sends it to the underlying drive system to complete the closed-loop control of a single control cycle.

[0022] In one exemplary embodiment, when performing steps 201-205, the specific steps may be as follows: Step S1: Establish a detailed mechanistic model of the permanent magnet synchronous motor as a foundation. In the digital twin unit, initialize the nominal physical parameters, including the moment of inertia J, the nominal viscous friction coefficient B0, and the permanent magnet flux linkage ψ. f and the extreme logarithm p n Configure the predictive control time-domain step number N, the cost function weight matrix, and the tolerance threshold ε=1.0rad / s for smooth dual-mode switching, and establish bidirectional synchronous sampling communication with the physical entity unit in hard real-time.

[0023] Step S2: During system operation, the embedded second-order nonlinear extended state observer (D-ESO) continues to run. It receives the actual mechanical angular velocity x1(k) and cross-axis current i of the physical entity at the current k-th sampling moment (k is the index of the discrete time step). q (k), the state equation of the nonlinear extended state observer is: ; In the formula, denoted as ω1, where ω2 is the first derivative of the observed angular velocity; b0 is the model nominal control gain estimate; c0 is the model nominal damping estimate; z2 is the observed value of the lumped disturbance w of the system; β1 is the first-order nonlinear extended state observer gain; and e1 is the observation error. Let β1 be the first derivative of the lumped disturbance observation, β2 be the second-order nonlinear extended state observer gain, z1 be the observed mechanical angular velocity, x1 be the real-time mechanical angular velocity, and i be the first derivative of the lumped disturbance observation. q It is the quadrature-axis current.

[0024] After obtaining the observed lumped disturbance value w(k), the equivalent viscous friction coefficient in the digital twin model is updated online using the statistically independent property of the external mechanical load and the motor speed, based on the gradient descent rule. Specifically, According to the formula Update the equivalent viscous friction coefficient; Among them, B p (k) represents the equivalent viscous friction coefficient at the current k-th sampling time, B p (k-1) is the equivalent viscous friction coefficient at the previous sampling time, k lr Let w(k) be the system lumped disturbance observation at the k-th sampling time, and x1(k) be the mechanical angular velocity at the k-th sampling time. Let T be the adaptive learning rate. s This is a discrete control cycle. The process forces the dissipation of cross-correlation energy, causing B to... p (k) Accurately approximates real physical damping.

[0025] Subsequently, according to the formula Extracting purely external high-frequency mechanical loads; In the formula, w d (k) represents a purely external high-frequency load, B m is the actual coefficient of viscous friction, and J is the moment of inertia.

[0026] Step S3: Decouple the equivalent viscous friction coefficient B obtained in step S2. p (k) and purely external high-frequency load w d (k) Synchronous injection dynamic twin module.

[0027] The predicted state at the next time step is derived using the following state-space equations. The formula expression for the full-band high-fidelity prediction model is as follows: ; In the formula, x(k+1) is the state prediction for the next time step, x(k) is the state variable for the current time step, and u(k) is the control variable for the current time step.

[0028] This prediction equation has completely eliminated the future effects of multiplicative bias, achieving high-fidelity model reconstruction across the entire frequency band.

[0029] Step S4: Within the same control cycle, based on the modified high-fidelity prediction model, two control law calculations are performed in parallel: For transient processes, Model Predictive Control (MPC) mode is used to construct a quadratic cost function H that covers the tracking error and control penalty in the prediction time domain. To avoid formula overflow, it is represented in line breaks as follows: ; In the formula, H is the quadratic cost function, i is the prediction step index, e(k+i|k) is the tracking error at step i, Q is the weight matrix of the tracking error, u(k+i|k) is the predicted control quantity at step i, R is the weight matrix of the control quantity, x(k+N|k) is the predicted terminal state, and F is the terminal state weight matrix. Substituting the prediction model into this function transforms it into a quadratic programming problem, according to the formula... Let the transient current value of the next cycle be obtained, where, and They are the prediction for the next time step and the actual time step. shaft current, For the current shaft current, For evaluation Axis voltage vector, The current rotor electric angular velocity, , , and These represent the stator resistance of the motor, respectively. Shaft inductance, Shaft inductance and permanent magnet flux linkage, This refers to the sampling period of the digital control system.

[0030] For steady-state processes, Digital Twin Direct Control (DTDC) modes are employed. The derivatives of the dynamic equations are set to zero, and the inverse is obtained through algebraic analysis of the twin dynamic equations, according to the formula... Determine the steady-state matching current command; In the formula, p n For the extreme logarithm, ω ref (k) is the reference angular velocity.

[0031] Step S5: Smooth switching and underlying computing power allocation based on the discriminant function. Calculate the algebraic deviation product between the two-modal outputs to construct the smooth switching discriminant function: ; In the formula, σ(k) is the smooth switching discrimination function, and e(k) is the speed tracking error. When the value of the smooth switching discrimination function σ(k) is greater than the threshold ε (the system is in acceleration / deceleration or a step transient), the following will be executed: Applied to physical entities, it provides extreme dynamic response; when the value of the smooth switching discrimination function σ(k) is less than or equal to the preset switching tolerance threshold ε, it is determined to be in a steady state, and the smooth switching discrimination function switches to... This mechanism eliminates the integral saturation jump during switching and reduces the steady-state time complexity from O(N³) to O(1), completely avoiding torque chattering caused by high-frequency iteration of the solver.

[0032] In one embodiment, such as Figure 3 As shown, a closed-loop control system architecture for a permanent magnet synchronous motor that integrates disturbance correlation separation and dynamic steady-state switching mechanisms is presented. Its core operating logic and symbol meaning can be summarized as follows: The system first receives the set reference angular velocity. With the actual feedback angular velocity of the motor The calculations are performed in a controller that incorporates model predictive control and digital twin direct control (DTDC), and then the final control command is output by switching logic and combining the "model reference" provided by the adaptive twin module. When the permanent magnet synchronous motor is in operation, the motor body is currently bearing the external mechanical load torque. It also possesses its inherent nominal coefficient of viscous friction. and rotor flux Meanwhile, control commands The actual speed of the motor as well as Shaft feedback current The data is synchronously fed into the observer and processed by the signal decorrelation mechanism, which extracts the lumped disturbance of the system into the equivalent viscous friction coefficient that is updated in real time. and purely external high-frequency mechanical loads These two dynamic variables are then input into the adaptive twin module to continuously update the high-fidelity prediction model, and the latest model reference closed loop is fed back to the front-end control and switching links, thereby achieving highly adaptive and precise servo control.

[0033] Figure 4 This paper presents a block diagram of a discrete-time motor control system, mainly composed of three core modules: physical entities, observers, and signal decorrelation. The physical entities section describes the motor's behavior under control input conditions. and external load disturbances A mathematical model of angular velocity difference under combined action; the observer module is based on the nominal model of the physical entity and uses the actual angular velocity sampled by the sensor. The error between the internal model prediction and the actual prediction is adjusted using closed-loop feedback (including proportional and integral components) to estimate the total external disturbance or unmodeled dynamics of the system in real time. The signal decorrelation module extracts the product of the actual rotational speed and the observer's output disturbance signal as the correlation index, which is then discretely integrated and superimposed with the initial value. Subsequently, it is used to identify or update specific time-varying parameters of the system online. Overall, this control architecture, through a combination of disturbance observation and online adaptive parameter estimation, aims to effectively improve the robustness of the motor system against external disturbances and internal parameter variations.

[0034] In one exemplary embodiment, a permanent magnet synchronous motor control device based on disturbance correlation separation and dynamic steady-state switching is disclosed, comprising: The lumped disturbance observation unit is used to collect real-time operating status data of the permanent magnet synchronous motor and obtain the lumped disturbance observation value of the system through the nonlinear extended state observer. The disturbance feature separation unit is used to separate the pure external high-frequency mechanical load by using a time-series signal correlation suppression mechanism to update the equivalent viscous friction coefficient online based on the lumped disturbance observation value and real-time angular velocity. The high-fidelity model reconstruction unit is used to substitute the equivalent viscous friction coefficient and the pure external high-frequency mechanical load into the dynamic twin module, correct the state prediction equation, and reconstruct the full-band high-fidelity prediction model. A dual-mode command calculation unit is used to perform parallel deduction of transient current commands for model prediction control and steady-state matching current commands for direct digital twin control based on the full-band high-fidelity prediction model. The smooth switching logic unit is used to perform non-integral switching between the transient current command and the steady-state matching current command based on the smooth switching discrimination function composed of the product of the dual-mode algebraic difference and the error, and output the determined control command to the underlying inverter.

[0035] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 6As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores defined control instructions. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network. When the computer program is executed by the processor, it implements a permanent magnet synchronous motor control method based on disturbance correlation separation and dynamic steady-state switching.

[0036] Figure 6 The structures shown are merely block diagrams of some structures related to the present application and do not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0037] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0038] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0039] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with relevant regulations and be authorized by the owner of the corresponding device.

[0040] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0041] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchain. The processors involved in the embodiments provided in this application may be, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc.

[0042] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0043] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A control method for a permanent magnet synchronous motor based on disturbance correlation separation and dynamic steady-state switching, characterized in that, include: Real-time operating status data of permanent magnet synchronous motors are collected, and the lumped disturbance observation values ​​of the system are obtained through a nonlinear extended state observer. The system is a permanent magnet synchronous motor control system based on disturbance correlation separation and dynamic steady-state switching; Based on the time-series signal correlation suppression mechanism, the equivalent viscous friction coefficient is updated according to the lumped disturbance observation value and real-time operating status data, and the pure external high-frequency mechanical load is extracted. Substituting the updated equivalent viscous friction coefficient and the pure external high-frequency mechanical load into the dynamic twin module, we obtain a full-band high-fidelity prediction model. Based on the full-band high-fidelity prediction model, the parallel computing model predicts the transient current command of the control mode and the steady-state matching current command of the digital twin direct control mode. Based on the smooth switching discrimination function, a non-integral switching is performed between the transient current command and the steady-state matching current command, and the determined control command is output to the underlying inverter.

2. The permanent magnet synchronous motor control method based on disturbance correlation separation and dynamic steady-state switching according to claim 1, characterized in that, The real-time operating status data includes real-time mechanical angular velocity and quadrature-axis current; the state equation of the nonlinear extended state observer is: ； In the formula, denoted as ω1, where ω2 is the first derivative of the observed angular velocity; b0 is the model nominal control gain estimate; c0 is the model nominal damping estimate; z2 is the observed value of the lumped disturbance w of the system; β1 is the first-order nonlinear extended state observer gain; and e1 is the observation error. Let β1 be the first derivative of the lumped disturbance observation, β2 be the second-order nonlinear extended state observer gain, z1 be the observed mechanical angular velocity, x1 be the real-time mechanical angular velocity, and i be the first derivative of the lumped disturbance observation. q It is the quadrature-axis current.

3. The permanent magnet synchronous motor control method based on disturbance correlation separation and dynamic steady-state switching according to claim 2, characterized in that, Based on the time-series signal correlation suppression mechanism, the equivalent viscous friction coefficient is updated according to the lumped disturbance observations and real-time operating status data, and the pure external high-frequency mechanical load is extracted, specifically including: According to the formula Update the equivalent viscous friction coefficient; According to the formula Extracting purely external high-frequency mechanical loads; Among them, B p (k) represents the equivalent viscous friction coefficient at the current k-th sampling time, B p (k-1) is the equivalent viscous friction coefficient at the previous sampling time, k lr Let w(k) be the system lumped disturbance observation at the k-th sampling time, and x1(k) be the mechanical angular velocity at the k-th sampling time. Let T be the adaptive learning rate. s For discrete control period, B m is the actual coefficient of viscous friction, and J is the moment of inertia.

4. The permanent magnet synchronous motor control method based on disturbance correlation separation and dynamic steady-state switching according to claim 3, characterized in that, The formula expression for the full-band high-fidelity prediction model is as follows: ； In the formula, x(k+1) is the state prediction for the next time step, x(k) is the state variable for the current time step, and u(k) is the control variable for the current time step.

5. The permanent magnet synchronous motor control method based on disturbance correlation separation and dynamic steady-state switching according to claim 4, characterized in that, Based on the full-band high-fidelity prediction model, the parallel computing model predicts the transient current command of the control mode and the steady-state matching current command of the digital twin direct control mode, specifically including: Based on the quadratic programming cost function, according to the formula This allows us to obtain the transient current command for the next cycle. The inverse of the twin dynamics equations is obtained through algebraic analysis, according to the formula... Determine the steady-state matching current command; In the formula, and They are the prediction for the next time step and the actual time step. shaft current, For the current shaft current, For evaluation Axis voltage vector, The current rotor electric angular velocity, , , and These represent the stator resistance of the motor, respectively. Shaft inductance, Shaft inductance and permanent magnet flux linkage, p is the sampling period of the digital control system. n For the extreme logarithm, ω ref (k) is the reference angular velocity.

6. The permanent magnet synchronous motor control method based on disturbance correlation separation and dynamic steady-state switching according to claim 5, characterized in that, When the value of the smooth switching discrimination function σ(k) is greater than the preset switching tolerance threshold ε, it is determined to be a transient phase, and the transient current command is output. When the value of the smooth switching discrimination function σ(k) is less than or equal to the preset switching tolerance threshold ε, it is determined to be in a steady state, and the steady state matching current command is output.

7. The permanent magnet synchronous motor control method based on disturbance correlation separation and dynamic steady-state switching according to claim 6, characterized in that, The formula expression for the smooth switching discrimination function is: ； In the formula, e(k) represents the speed tracking error. This is a transient current command.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that the processor executes the computer program to implement a permanent magnet synchronous motor control method based on disturbance correlation separation and dynamic steady-state switching as described in any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements a permanent magnet synchronous motor control method based on disturbance correlation separation and dynamic steady-state switching as described in any one of claims 1-7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements a permanent magnet synchronous motor control method based on disturbance correlation separation and dynamic steady-state switching as described in any one of claims 1-7.

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