Pmslm motor time delay control method and system based on adaptive gain driving and medium

Abstract

The present application relates to permanent magnet synchronous motor technology field, especially the PMSLM motor time delay control method, system and medium based on adaptive gain power, the dynamics order model of the position-current double closed loop system of PMSLM motor is established, the position tracking error is defined and the position error dynamics equation is derived, the expected position tracking error dynamics is designed, and the time delay equivalent control law is solved based on the equivalent control idea, the system lumped uncertainty is estimated and filtered on line by using time delay estimation method, the effective compensation of parameter uncertainty, external disturbance and unmodeled dynamic is realized, and the time delay control law under fixed gain is obtained, the adaptive gain dynamics based on Lyapunov stability analysis is introduced, and the on-line adaptive adjustment of control gain is realized.The present application realizes the adaptive gain dynamics PMSLM motor servo control and optimization, and significantly improves the position tracking accuracy, robustness and working condition adaptability of the motor.

Description

Technical Field

[0001] This invention relates to the field of permanent magnet synchronous motor technology, and in particular to a PMSLM motor delay control method, system and medium based on adaptive gain dynamics. Background Technology

[0002] Permanent magnet synchronous linear motors (PMSLMs) offer advantages such as fast dynamic response and high positioning accuracy, leading to their widespread application in servo applications such as high-end CNC machine tools, industrial robots, and automated logistics transport equipment. However, uncertainties such as nonlinear friction, external disturbances, parameter variations, and unmodeled dynamics can cause a decrease in control accuracy. To achieve satisfactory servo performance for PMSLMs, sliding mode control, active disturbance rejection control, and model predictive control have attracted considerable attention from researchers. However, most of these methods are based on mathematical models that incorporate prior knowledge of the system. This poses a challenge for the highly nonlinear and strongly coupled PMSLM systems and their complex operating conditions.

[0003] In contrast, time-delay control only requires the order and structure of the PMSLM system, utilizing past control input current and acceleration information to eliminate complex unmodeled dynamics, intractable nonlinearities, and external disturbances. When compensating for PMSLM dynamics using time-delay estimation, the required position error dynamics can be explicitly specified and injected into the system's closed-loop dynamics, thereby achieving the desired position tracking performance. However, traditional time-delay control strategies employ a fixed control gain. Too small a constant gain leads to poor tracking accuracy, while too large a gain results in oscillating response or instability. Therefore, in practical applications, it requires manual adjustment through trial and error, which demands significant time and effort. Furthermore, once the system load changes, a fixed control gain cannot achieve optimal control performance, necessitating manual readjustment and resulting in poor applicability to various operating conditions. Summary of the Invention

[0004] This invention overcomes the shortcomings of the prior art and provides a PMSLM motor delay control method, system and medium based on adaptive gain dynamics.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] The first aspect of this invention provides a PMSLM motor delay control method based on adaptive gain dynamics, comprising the following steps:

[0007] S01: Establish the dynamic order model of the PMSLM motor position-current dual closed-loop system, define the position tracking error of the PMSLM motor, derive the position tracking error through the dynamic order model, and generate the position error dynamic equation of the PMSLM motor.

[0008] S02: Construct a linear integral sliding surface, derive the desired position tracking error dynamics on the linear integral sliding surface based on the boundary layer thickness of the dynamically constrained sliding variables, and import the position error dynamics equation into the desired control input to solve according to the equivalent control idea, so as to obtain the equivalent control law for the time delay of the PMSLM motor.

[0009] S03: Collect historical position and control data of PMSLM motor, construct a time-series dynamic incremental model based on historical position and control data, use time delay estimation method to estimate and filter the lumped uncertainty of motor system online in time-series dynamic incremental model, output time delay estimation term, sum the equivalent control law and time delay estimation term to initially obtain the compensation control law of time delay controller under fixed gain;

[0010] S04: Set an adjustable adaptive gain term, determine the boundary layer projection operator limit adaptive gain term based on the sliding mode variable boundary layer thickness, and perform time derivative and judgment analysis of the Lyapus function. Design an adaptive gain dynamic to replace the fixed gain of the compensation control law, and automatically adjust the control gain online to adapt to different operating conditions of the PMSLM motor.

[0011] Furthermore, in a preferred embodiment of the present invention, step S01 specifically includes the following steps:

[0012] Acquire mover position data, input current data, and one or more uncertainty data of the system lumped together for the PMSLM motor; wherein, the uncertainty data includes parameter uncertainty, thrust fluctuation, nonlinear friction, load disturbance, and unmodeled dynamics;

[0013] By controlling the dynamic coupling of the mover position data, input current data and various uncertain data through the driving position loop and current loop, a dynamic order model of the PMSLM motor position-current dual closed-loop system is established.

[0014] Obtain the predetermined position setpoint of the PMSLM motor, and define the position tracking error variable of the motor based on the predetermined position setpoint;

[0015] The position tracking error variables are imported into the dynamic order model for dynamic derivation, and the position error dynamic equation of the PMSLM motor is output.

[0016] Furthermore, in a preferred embodiment of the present invention, step S02 specifically includes the following steps:

[0017] The sliding mode state of the position tracking error of the PMSLM motor from the initial moment to the arrival stage under dynamic time-varying conditions is captured to dynamically constrain the boundary layer thickness of the sliding mode variable, and the sliding mode saturation function is preset based on the boundary layer thickness.

[0018] Based on position tracking error variables Construct a first-order linear integral sliding surface, use a continuously positioned sliding saturation function on the linear integral sliding surface to replace the discontinuous position tracking sign function, and derive the control law for the sliding variable;

[0019] In the derivation of the control law, an exponential reaching law for the motor system is set, and the sliding mode is made to satisfy the desired arrival condition of the exponential reaching law. The desired control input for the position tracking error dynamics is obtained, and the formula is:

[0020] ;

[0021] In the formula: The second derivative of the position tracking error, The first derivative of the position tracking error. For position tracking error, For the convergence rate coefficient of the exponential reaching law;

[0022] By introducing the equivalent control concept, the position error dynamic equation is substituted into the desired control input, and finally the equivalent control law for the time delay of the PMSLM motor is obtained.

[0023] Furthermore, in a preferred embodiment of the present invention, step S03 specifically includes the following steps:

[0024] Collect the current running position status of the PMSLM motor at a preset time node, and extract the historical running position status and corresponding historical control quantity of the previous time adjacent to the preset time node;

[0025] By combining the current operating position status, the historical operating position status of the previous moment, and historical control variables, the system increment of the PMSLM motor is discretely modeled to construct the time-series dynamic incremental model of the motor.

[0026] The system lumped uncertainty of the motor is estimated online using a first-order dynamic filter structure in the time-series dynamic incremental model through time delay estimation technology. This yields piecewise continuous incremental estimates of the uncertainty. The equivalent control law then feeds back the incremental estimates from the previous time step to calculate the control disturbance of the dynamic order model after time t0, where the uncertainty has a time delay. This results in the lumped disturbance value of the motor system within the first time period t0, as shown in the formula:

[0027] ;

[0028] In the formula: For speed, For acceleration, For the delayed time Shaft reference current, Position status, For equivalent control gain;

[0029] By combining the lumped disturbance value and the incremental estimate of the uncertainty term, the uncertainty estimate of the PMSLM motor at the current moment is obtained under the condition of time delay estimation.

[0030] The uncertainty estimate is summed with the equivalent control law for the time delay of the PMSLM motor to generate the compensation control law for the time delay controller under fixed gain.

[0031] Furthermore, in a preferred embodiment of the present invention, the method of estimating the lumped uncertainty of the motor system online using a first-order dynamic filtering structure in the time-series dynamic incremental model through time delay estimation technology, obtaining piecewise continuous incremental estimates of the uncertainty, and using the equivalent control law feedback to calculate the control disturbance of the dynamic order model after time t0 when the uncertainty occurs due to time delay based on the incremental estimates of the previous time, to obtain the lumped disturbance value of the motor system in the first time period t0, specifically includes the following steps:

[0032] By introducing time delay estimation technology and constructing a first-order dynamic filtering structure, the continuous control changes within adjacent sampling times are approximated to the control changes of unknown dynamics and disturbances in the time-series dynamic incremental model, thus enabling online estimation of the system lumped uncertainty of the PMSLM motor.

[0033] In the uncertain sampling period of a motor position state, the unknown changes slowly. At this time, the differential noise is easily amplified by discrete means. The first-order dynamic filter structure can detect and filter out the chattering caused by differential amplification noise and output relatively smooth incremental estimates of continuous uncertain terms in different segments.

[0034] The incremental estimate of the continuous uncertainties in the segmented phase of the previous adjacent time is obtained and defined as the preceding incremental estimate. Based on the preceding incremental estimate, the dynamic order model of the PMSLM motor position-current dual closed-loop system is pushed forward to time t0.

[0035] After pushing forward, the control disturbance caused by the lumped uncertainty of the motor system due to the time delay of the position state is calculated by using the equivalent control law feedback, and the lumped disturbance value of the motor system in the first t0 time period is obtained.

[0036] Furthermore, in a preferred embodiment of the present invention, step S04 specifically includes the following steps:

[0037] The error variables of the PMSLM motor under different operating control conditions on the linear integral sliding surface are extracted by the position error tracking log and defined as the generalized sliding mode variable set.

[0038] Based on the preset adaptive gain dynamic coefficients of the operation control conditions, the boundary layer projection operator is determined according to the boundary layer thickness of the dynamic boundary constraints of the generalized sliding mode variable set.

[0039] An adaptively adjustable adaptive gain term is set on the linear integral sliding surface. The adaptive gain term is limited by the boundary layer projection operator and injected into the Lyapus function stack. The time derivative and convergence stability are determined by the adaptive gain dynamic coefficients. Based on the Lyapus determination results, an online update term for the adaptive gain dynamics is constructed.

[0040] By replacing the fixed gain in the compensation control law with an online shape-changing term based on adaptive gain dynamics, a time delay control law for the PMSLM motor based on adaptive gain dynamics is finally obtained. This PMSLM motor time delay control law is used to adapt to the position tracking of the PMSLM motor under different operating control conditions.

[0041] Furthermore, in a preferred embodiment of the present invention, the step of setting an adaptively adjustable adaptive gain term on the linear integral sliding surface, using a boundary layer projection operator to limit the adaptive gain term, and injecting it into the Lyapus function stack for time differentiation and convergence stability determination through adaptive gain dynamics coefficients, and constructing an online update term for adaptive gain dynamics based on the Lyapus determination result, specifically includes the following steps:

[0042] Construct a Lyapus function stack, set an adaptive gain term with an adjustable power-law parameter that varies with the position tracking error on the linear integral sliding surface, perform projection limiting on the adaptive gain term based on the boundary layer projection operator, and add the adaptive gain term to the Lyapus function stack for storage after projection limiting.

[0043] The Lyapus function stack storing the adaptive gain term is differentiated in time based on the adaptive gain dynamics coefficients. During the differentiation process, the lumped uncertainty term of the motor system is canceled out, and the Lyapus exponent of the adaptive gain dynamics is output.

[0044] When the Lyapus exponent is less than or equal to 0, the dynamic changes of the generalized sliding mode variables under different operating control conditions are strongly coupled with the boundary layer states corresponding to each variable, constructing a control input form for gain leakage scheduling and generating an adaptive gain dynamics online update term.

[0045] A second aspect of the present invention provides a PMSLM motor delay control system based on adaptive gain dynamics, applicable to any of the PMSLM motor delay control methods based on adaptive gain dynamics described in any one of the claims. The system specifically includes:

[0046] Rectifier module: The rectifier module is used to convert external AC power into a stable DC voltage to provide DC bus power to the inverter module;

[0047] Inverter module: The inverter module is responsible for converting the DC voltage output by the rectifier module into an AC voltage with controllable amplitude, frequency and phase, for driving the PMSLM motor;

[0048] Servo Control Unit: The servo control unit adopts a dual-loop structure with a cascaded position loop and a current loop. The unit includes a time delay controller based on adaptive gain dynamics. Two PI controllers are used in the current loop to generate the control voltage required to drive the PMSLM motor and complete the online calculation of the control algorithm.

[0049] Position encoder module: The position encoder module is used to collect the position and speed information of the PMSLM motor in real time, and obtain the speed information through numerical differentiation or filtering algorithms, and feed it back to the servo control unit;

[0050] PMSLM Motor: The PMSLM motor is responsible for servo action execution under the inverter output voltage, realizing precise position control of the load.

[0051] A third aspect of the present invention provides a computer-readable storage medium comprising a PMSLM motor delay control method program based on adaptive gain dynamics, wherein when the PMSLM motor delay control method program based on adaptive gain dynamics is executed by a processor, the steps of any of the PMSLM motor delay control methods based on adaptive gain dynamics described in the present invention are implemented.

[0052] This invention addresses the technical deficiencies in the prior art, and its beneficial technical effects are as follows:

[0053] This invention organically combines time delay control with adaptive gain dynamics. By constructing an adaptive gain evolution mechanism that includes injection and leakage terms, the control gain can be automatically adjusted online according to real-time changes in position tracking error, avoiding the problem of relying on manual trial and error tuning in traditional fixed-gain time delay control. When system uncertainty or external disturbances increase, the control gain can be rapidly increased to enhance robustness; when the tracking error decreases, the gain automatically decays, effectively suppressing noise amplification and control chattering, thereby achieving smooth and stable control input. This invention can accurately compensate for lumped uncertainties such as parameter uncertainty, nonlinear friction, and load disturbances without requiring a precise system model. Especially under load changes and sudden disturbances, it can still maintain excellent dynamic and steady-state performance, significantly improving the position tracking accuracy, disturbance rejection performance, and operating condition adaptability of the PMSLM motor, achieving the goal of optimizing the position tracking performance of the PMSLM motor to meet the needs of different operating conditions. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained from these drawings without creative effort.

[0055] Figure 1 A flowchart of the PMSLM motor delay control method based on adaptive gain dynamics is shown.

[0056] Figure 2 A system framework diagram of a PMSLM motor delay control system based on adaptive gain dynamics is shown.

[0057] Figure 3 The circuit topology diagram of the time delay controller based on adaptive gain dynamics is shown. Detailed Implementation

[0058] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0059] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0060] The first aspect of this invention provides a PMSLM motor delay control method based on adaptive gain dynamics, such as... Figure 1 As shown, it includes the following steps:

[0061] S01: Establish the dynamic order model of the PMSLM motor position-current dual closed-loop system, define the position tracking error of the PMSLM motor, derive the position tracking error through the dynamic order model, and generate the position error dynamic equation of the PMSLM motor.

[0062] S02: Construct a linear integral sliding surface, derive the desired position tracking error dynamics on the linear integral sliding surface based on the boundary layer thickness of the dynamically constrained sliding variables, and import the position error dynamics equation into the desired control input to solve according to the equivalent control idea, so as to obtain the equivalent control law for the time delay of the PMSLM motor.

[0063] S03: Collect historical position and control data of PMSLM motor, construct a time-series dynamic incremental model based on historical position and control data, use time delay estimation method to estimate and filter the lumped uncertainty of motor system online in time-series dynamic incremental model, output time delay estimation term, sum the equivalent control law and time delay estimation term to initially obtain the compensation control law of time delay controller under fixed gain;

[0064] S04: Set an adjustable adaptive gain term, determine the boundary layer projection operator limit adaptive gain term based on the sliding mode variable boundary layer thickness, and perform time derivative and judgment analysis of the Lyapus function. Design an adaptive gain dynamic to replace the fixed gain of the compensation control law, and automatically adjust the control gain online to adapt to different operating conditions of the PMSLM motor.

[0065] Furthermore, in a preferred embodiment of the present invention, step S01 specifically includes the following steps:

[0066] Acquire mover position data, input current data, and one or more uncertainty data of the system lumped together for the PMSLM motor; wherein, the uncertainty data includes parameter uncertainty, thrust fluctuation, nonlinear friction, load disturbance, and unmodeled dynamics;

[0067] By controlling the dynamic coupling of the mover position data, input current data, and various uncertain data through the driving position loop and current loop, a dynamic order model of the PMSLM motor position-current dual closed-loop system is established, and the formula is:

[0068] ;

[0069] In the formula: For the position of the mover, To control the input current, For the gain of the system to be designed, This indicates that the total uncertainty of the system set is not yet clear.

[0070] Obtain the predetermined position setpoint of the PMSLM motor, and define the motor's position tracking error variable based on the predetermined position setpoint. The formula is:

[0071] ;

[0072] In the formula: Set the motor position value;

[0073] The position tracking error variables are imported into the dynamic order model for dynamic derivation, and the position error dynamic equation of the PMSLM motor is output.

[0074] It should be noted that by establishing a dynamic order model of the PMSLM motor position-current dual closed-loop system, the high-order, strongly coupled PMSLM motor system is equivalent to a clear first-order dynamic structure. The model covers the nonlinearity, time-varying nature, and external disturbances present in the actual system, and uniformly represents the unknown disturbances as the system's lumped uncertainty term. This algorithm comprehensively expresses complex electromagnetic, mechanical, and disturbance factors, significantly simplifying the controller design process and effectively reducing the excessive reliance of error control on precise system parameters. Furthermore, by establishing an error description mechanism between the control objective and the motor system state, this algorithm formalizes the control objective from "position tracking" into a sub-problem of "error approaching zero," laying the foundation for a gain control design approach centered on position tracking error dynamics. This makes the control system design more targeted and analyzable, providing input for the adaptive gain mechanism. The specific formula for the position error dynamics equation of the PMSLM motor is as follows:

[0075] ;

[0076] This formula organically unifies and reflects the quantitative relationship between position error, control input, and motor uncertainty, indicating that adjusting the control input current... It can directly affect the evolution of error and reveal the system gain. This system plays a crucial role in suppressing uncertainties and regulating motor dynamic performance, supporting online compensation for uncertainties and improving system tracking accuracy and stability. The position error dynamic equation directly describes the evolution of motor position tracking error, explicitly responding to the impact of system uncertainties on position tracking error, and realizing the transformation from motor system state to error dynamics.

[0077] Furthermore, in a preferred embodiment of the present invention, step S02 specifically includes the following steps:

[0078] The sliding mode state of the position tracking error of the PMSLM motor from the initial moment to the arrival stage under dynamic time-varying conditions is captured to dynamically constrain the boundary layer thickness of the sliding mode variable, and the sliding mode saturation function is preset based on the boundary layer thickness.

[0079] A first-order linear integral sliding surface is constructed based on the position tracking error variable. The continuous positioning sliding saturation function is used to replace the discontinuous position tracking sign function on the linear integral sliding surface, and the control law of the sliding variable is derived.

[0080] In the derivation of the control law, an exponential reaching law for the motor system is set, and the sliding mode is made to satisfy the desired arrival condition of the exponential reaching law. The desired control input for the position tracking error dynamics is obtained, and the formula is:

[0081] ;

[0082] In the formula: The second derivative of the position tracking error, The first derivative of the position tracking error. For position tracking error, For the convergence rate coefficient of the exponential reaching law;

[0083] By introducing the equivalent control concept, the position error dynamic equation is substituted into the desired control input, and finally the equivalent control law for the time delay of the PMSLM motor is obtained.

[0084] It should be noted that the main purpose of designing a first-order linear integral sliding surface is to achieve high accuracy and rapid convergence of the position tracking error of the PMSLM motor. The specific design formula for the sliding surface is as follows:

[0085] ;

[0086] In the formula: For sliding mode variables, The first derivative of the position tracking error, The sliding surface coefficient is denoted by . This linear integral sliding surface introduces the concept of error integration, which eliminates steady-state error through the integral term, ensuring that the system satisfies s(0)=0 at t=0, thus improving the steady-state accuracy and effectively suppressing constant disturbances. Specifically, by acquiring the sliding state evolution characteristics of the PMSLM motor during dynamic operation in real time, and based on the changes in sliding variables during the start-up, transition, and stabilization phases of the position tracking error, the entire process of the sliding variables moving from away from the sliding surface to entering the sliding surface is monitored. Simultaneously, the boundary layer thickness of the sliding variables is dynamically constrained, allowing the boundary layer thickness to change dynamically with the magnitude of the error, rather than remaining fixed. This enhances the control effect during periods of large errors and smooths the control output during periods of small errors. The adjustability of the boundary layer thickness adapts to the dynamic performance requirements under different operating conditions, overcoming the limitation of fixed boundary layer sliding mode control in complex operating conditions where it is difficult to balance response speed and smoothness. Among them, the sliding mode saturation function with preset boundary layer thickness can smoothly transition and significantly reduce high-frequency switching noise of control input; using the saturation function to replace the sign function makes the control input continuous and smooth, effectively avoiding the high-frequency chattering problem caused by discontinuous switching in traditional sliding mode control. While ensuring rapid arrival at the sliding surface, it improves the control smoothness in the steady state stage and enhances the stability and robustness of the motor system under dynamic time-varying conditions. This is the key link and technical support for enabling PMSLM motors to achieve high-performance servo control in this invention.

[0087] It should be noted that the exponential reaching law enables the sliding mode control to possess predictable and designable dynamic characteristics of the system's convergence speed and transient response. Furthermore, the desired arrival condition specifically allows for... By adjusting the sliding surface coefficient The system response speed can be flexibly adjusted to achieve the desired dynamic modeling effect of position tracking error dynamics, i.e., the desired control input of position tracking error dynamics. This clarifies the dynamic characteristics and convergence speed of the error system, ensuring that the position tracking error converges rapidly in an exponential manner. A clear convergence law is set for the dynamic process of the sliding mode variable reaching the sliding surface, improving the system response speed and shortening the sliding mode arrival time. Using the equivalent control concept, the error dynamics of the actual system are matched with the desired error dynamics, deriving the time-delay equivalent control law that satisfies the sliding mode arrival condition. The specific formula of the equivalent control law is as follows:

[0088] ;

[0089] This algorithm achieves a precise approximation of the desired error dynamics within the actual system, compensating for the system's lumped uncertainty through equivalent control terms, thus forming a control input expression directly applicable to motor drive. By designing the dynamics of the desired position tracking error, the algorithm derives the equivalent control law, thereby designing a time delay controller for the PMSLM motor that can effectively compensate for disturbances in real time, provide time delay estimation, and achieve high position error tracking accuracy.

[0090] Furthermore, in a preferred embodiment of the present invention, step S03 specifically includes the following steps:

[0091] Collect the current running position status of the PMSLM motor at a preset time node, and extract the historical running position status and corresponding historical control quantity of the previous time adjacent to the preset time node;

[0092] By combining the current operating position status, the historical operating position status of the previous moment, and historical control variables, the system increment of the PMSLM motor is discretely modeled to construct the time-series dynamic incremental model of the motor.

[0093] The system lumped uncertainty of the motor is estimated online using a first-order dynamic filter structure in the time-series dynamic incremental model through time delay estimation technology. This yields piecewise continuous incremental estimates of the uncertainty. The equivalent control law then feeds back the incremental estimates from the previous time step to calculate the control disturbance of the dynamic order model after time t0, where the uncertainty has a time delay. This results in the lumped disturbance value of the motor system within the first time period t0, as shown in the formula:

[0094] ;

[0095] In the formula: For speed, For acceleration, For the delayed time Shaft reference current, Position status, For equivalent control gain;

[0096] By substituting the lumped disturbance value and the incremental estimate of the uncertainty term, the uncertainty estimate of the PMSLM motor at the current moment based on the time delay estimation condition is obtained, as shown in the formula:

[0097] ;

[0098] In the formula: For speed, For acceleration, For the delayed time Shaft reference current, Position status, For equivalent control gain;

[0099] The uncertainty estimate is summed with the equivalent control law for the time delay of the PMSLM motor to generate the compensation control law for the time delay controller under fixed gain.

[0100] It should be noted that by discretely modeling the dynamic behavior of the motor system in an incremental form, the absolute form of the complex nonlinearity and uncertainty of the system in adjacent sampling periods is mapped to a quantifiable incremental form. This allows for a sample-by-sample characterization of the system's dynamic error characteristics, achieving an effective transition from continuous dynamics to a discrete control framework. Furthermore, since incremental modeling inherently possesses a high degree of suppression capability for constant disturbances and slowly varying uncertainties, it can significantly reduce the influence of initial errors and modeling biases, which is beneficial to improving the stability and numerical reliability of subsequent time delay estimation. Further, this algorithm utilizes time delay estimation to perform online estimation of the system's lumped uncertainty, achieving real-time estimation of piecewise continuous uncertainty. Simultaneously, a constructed first-order dynamic filter structure is used for smoothing, suppressing the influence of measurement noise on the disturbance estimation results. The algorithm also backtracks to obtain disturbance information from the previous sampling period and uses historical control and state information to construct a time delay disturbance compensation term. This allows the system to "look back" at the sudden disturbance behavior through historical data, fully leveraging the technical advantages of time delay estimation and laying a crucial foundation for disturbance compensation in the subsequent control law. The formula is: It quantitatively provides the calculation method of the system lumped disturbance in the previous sampling period, and directly interprets the system uncertainty by back-inferring from the prior historical control input and historical position state, avoiding explicit modeling or identification of the uncertainty disturbance. The estimation accuracy of time delay naturally improves as the sampling period shortens. This lumped disturbance value transforms the system uncertainty into a calculable and compensable control quantity.

[0101] It should be noted that by pushing the historical lumped disturbance estimation results forward to the current moment, under the assumption of time delay, an approximate estimate of the current system's lumped uncertainty is obtained, i.e., the uncertainty estimate. This leverages the characteristic that system uncertainty changes continuously or slowly over a short period to achieve real-time approximate reconstruction of the current disturbance. It provides a rapid and sensitive response to parameter changes and load abrupt changes without requiring real-time disturbance measurement, improving the feedforward compensation effect of the control system for uncertainty. Furthermore, from the above formula: It can be observed that, in order to obtain To estimate the uncertainty, we only need to know the current input and linear acceleration of the PMSLM motor at the previous moment. In the actual digital implementation, the minimum value of t0 is chosen as the system sampling period; the smaller the sampling period, the more accurate the uncertainty estimate. By adding the uncertainty estimate to the equivalent control law of the PMSLM motor time delay, we can obtain the compensation control law of the time delay controller under fixed gain. The specific formula is:

[0102] ;

[0103] Furthermore, in a preferred embodiment of the present invention, the method of estimating the lumped uncertainty of the motor system online using a first-order dynamic filtering structure in the time-series dynamic incremental model through time delay estimation technology, obtaining piecewise continuous incremental estimates of the uncertainty, and using the equivalent control law feedback to calculate the control disturbance of the dynamic order model after time t0 when the uncertainty occurs due to time delay based on the incremental estimates of the previous time, to obtain the lumped disturbance value of the motor system in the first time period t0, specifically includes the following steps:

[0104] By introducing time delay estimation technology and constructing a first-order dynamic filtering structure, the continuous control changes within adjacent sampling times are approximated to the control changes of unknown dynamics and disturbances in the time-series dynamic incremental model, thus enabling online estimation of the system lumped uncertainty of the PMSLM motor.

[0105] In the uncertain sampling period of a motor position state, the unknown changes slowly. At this time, the differential noise is easily amplified by discrete means. The first-order dynamic filter structure can detect and filter out the chattering caused by differential amplification noise and output relatively smooth incremental estimates of continuous uncertain terms in different segments.

[0106] The incremental estimate of the continuous uncertainties in the segmented phase of the previous adjacent time is obtained and defined as the preceding incremental estimate. Based on the preceding incremental estimate, the dynamic order model of the PMSLM motor position-current dual closed-loop system is pushed forward to time t0.

[0107] ;

[0108] In the formula: For the current moment, This is the amount of time delay. Let 't' be the lumped disturbance at the current time t. The lumped disturbance at the time delay t-t0;

[0109] After pushing forward, the control disturbance caused by the lumped uncertainty of the motor system due to the time delay of the position state is calculated by using the equivalent control law feedback, and the lumped disturbance value of the motor system in the first t0 time period is obtained.

[0110] It should be noted that, based on the assumption that the unknown terms change slowly over a short period, the estimation logic is simplified. The previously difficult-to-model nonlinearities, couplings, and external disturbances are uniformly mapped to control increments that can be estimated online. This allows the lumped uncertainty of the PMSLM motor system to be estimated online without explicit modeling. Furthermore, by utilizing the system's current or adjacent sampling time state and control input information, a real-time approximation of the unknown delay term is performed, enabling the lumped uncertainty of the PMSLM motor system to be estimated online without explicit modeling. This quickly obtains a "coarse-grained" estimation matrix of the time-delay disturbance, ensuring that the online estimation mechanism closely follows changes in the motor system. It is worth mentioning that this algorithm introduces a first-order dynamic filtering structure, the specific formula of which is:

[0111] ;

[0112] In the formula: The cutoff frequency, This is the filtered time delay estimate. This represents the original time delay estimate. This first-order dynamic filtering structure can suppress and smooth high-frequency noise carried over in the online estimation process, even when the uncertain unknown changes slowly and measurement noise may be amplified by differential operations. It significantly eliminates noise amplification caused by differential estimation operations, preventing high-frequency noise from being misjudged as system disturbances and causing oscillations in the control input. It also reduces the probability of abrupt changes in the time delay estimation curve. Compared to traditional time delay estimation methods, it effectively improves the smoothness and continuity of the uncertain estimation signal under piecewise continuous conditions, and solves the problem of chattering and control performance degradation easily caused by traditional methods in noisy environments. This ensures high reliability and accuracy of the lumped uncertainty online estimation results.

[0113] It should be noted that online estimation of the lumped uncertainty of the PMSLM motor system This invention employs time delay estimation techniques to estimate... Its basic idea is to assume It can be continuous or segmented continuous for a sufficiently small time interval t0. The value at time t will be infinitely close to the value at time t. The values ​​at times t to t0. Therefore, this algorithm advances the historical estimates to the current time by pushing forward the dynamic order model of the motor system to time t0, establishing a mapping relationship between historical uncertainty and the current system state. The time-push operation reflects the time delay effect of the propagation of uncertainty disturbances in the system, thus using the historical estimation results as the basis for predicting the current uncertainty disturbance, realizing the transmission of motor system uncertainty from "historical position and control quantity information" to "current control decision"; the estimated values ​​at previous and subsequent times are closely correlated, effectively avoiding the occurrence rate of jumps. The actual disturbance value experienced by the system at the previous time is reconstructed, i.e., the formula: This formula clarifies the fundamental assumptions for time delay estimation: that within a sufficiently small sampling period, the system's lumped uncertainty remains approximately constant or changes slowly between adjacent moments. This allows the actual error dynamics of the motor system to approximate the desired error dynamics, and provides a stable foundation for subsequent adaptive gain or robust control strategies for the PMSLM motor.

[0114] Furthermore, in a preferred embodiment of the present invention, step S04 specifically includes the following steps:

[0115] The error variables of the PMSLM motor under different operating control conditions on the linear integral sliding surface are extracted by the position error tracking log and defined as the generalized sliding mode variable set.

[0116] Based on the preset adaptive gain dynamic coefficients of the operation control conditions, the boundary layer projection operator is determined according to the boundary layer thickness of the dynamic boundary constraints of the generalized sliding mode variable set.

[0117] An adaptively adjustable adaptive gain term is set on the linear integral sliding surface. The adaptive gain term is limited by the boundary layer projection operator and injected into the Lyapus function stack. The time derivative and convergence stability are determined by the adaptive gain dynamic coefficients. Based on the Lyapus determination results, an online update term for the adaptive gain dynamics is constructed.

[0118] By replacing the fixed gain in the compensation control law with an online shape-changing term based on adaptive gain dynamics, a time delay control law for the PMSLM motor based on adaptive gain dynamics is finally obtained. This PMSLM motor time delay control law is used to adapt to the position tracking of the PMSLM motor under different operating control conditions.

[0119] It should be noted that, considering the gain This involves a trade-off between minimizing the impact of time delay estimation errors and reducing signal noise. In practical applications, it is manually adjusted through trial and error, which requires significant time and effort. Furthermore, once the system load changes, a fixed dynamic gain cannot achieve optimal control performance, necessitating manual readjustment and resulting in poor applicability to various operating conditions. To address this, this algorithm designs adaptive gain dynamics to adapt to different operating control conditions of the PMSLM motor and achieves robust performance against disturbances. By collecting and aggregating the dispersed position tracking error behaviors under multiple operating conditions into a comprehensive error set, the convergence of multiple errors is simplified to convergence of a single sliding mode variable, providing quantifiable and comparable state indicators for subsequent adaptive gain adjustment. Meanwhile, to suppress gain divergence, this algorithm further formulates a boundary layer projection operator based on the boundary layer thickness of the generalized sliding mode variable. This boundary layer projection operator introduces a negative feedback term proportional to the current gain into the adaptive law, continuously suppressing and attenuating the adaptive gain growth, thus limiting the adaptive gain adjustment to a controllable boundary layer range. Specifically, when the system error decreases or enters the steady-state region, the gain gradually falls back to a reasonable range, avoiding the unbounded growth of the adaptive gain due to long-term injection or transient and noise effects. This effectively solves the problems of gain drift and chattering aggravation in traditional pure injection adaptive laws, achieving dynamic matching between the adaptive adjustment amplitude and the system operating conditions, enabling the PMSLM control system to maintain high stability and smoothness under different load, speed, and disturbance stages.

[0120] It should be noted that, under the constraint of the boundary layer projection operator on the adaptive gain term, the online adjustable adaptive gain update process is constrained and guided by Lyapus stability theory within the integral sliding mode control framework. This constructs stable and controllable adaptive gain dynamics, enabling the control gain to adaptively increase or decrease with the sliding mode state, suppressing drastic gain fluctuations caused by system lumped uncertainties or noise. Furthermore, constructing the update term based on the stability determination results ensures that the adaptive gain update has a clear convergence direction and speed, updating the gain in each control cycle to adapt to or highly adapt to system changes in real time. Adaptive gain dynamics can significantly reduce the workload of manual parameter tuning, avoid the sub-problem of balancing fast and smooth response under different operating conditions with fixed gain, and significantly improve the consistency of position tracking performance of the PMSLM system.

[0121] Regarding the adaptive gain mechanism, the specific formula is as follows:

[0122] ;

[0123] In the formula: For adaptive gain, for The first-order differential, >0, >0, >1 represents the adaptive gain dynamics coefficient, used to adjust the rate of change of the adaptive gain; To prevent parameter drift, As a small positive number, The lower limit value to prevent adaptive gain Less than or equal to zero For standard symbolic functions, satisfying:

[0124] ;

[0125] In the formula: The dependent variable is a symbolic function.

[0126] By replacing the fixed gain in the compensation control law with adaptive gain dynamics, the specific formula for the PMSLM motor time delay control law based on adaptive gain dynamics is obtained as follows:

[0127] ;

[0128] The proposed adaptive mechanism: In adaptive gain dynamics: the first term This represents the injection term generated based on the dynamic changes of the sliding mode variable, and the last term... This indicates a leaked item. The dynamic changes will increase the gain due to the injection term when the sliding mode variable is large. When the sliding mode variable is small, the injection term will become smaller, and the leakage term will dominate the overall dynamic changes. This also decreases accordingly. Therefore, the proposed adaptive gain dynamic mechanism effectively avoids overestimation of the gain value due to leakage terms, and the control input is jitter-free. Through parameters... and The boundary layer thickness of the sliding mode variable can be adjusted. Because the sliding mode variable can be considered as tracking error, It will automatically adjust to optimize the position tracking performance of PMSLM.

[0129] Furthermore, in a preferred embodiment of the present invention, the step of setting an adaptively adjustable adaptive gain term on the linear integral sliding surface, using a boundary layer projection operator to limit the adaptive gain term, and injecting it into the Lyapus function stack for time differentiation and convergence stability determination through adaptive gain dynamics coefficients, and constructing an online update term for adaptive gain dynamics based on the Lyapus determination result, specifically includes the following steps:

[0130] Construct a Lyapus function stack, set an adaptive gain term with an adjustable power-law parameter that varies with the position tracking error on the linear integral sliding surface, perform projection limiting on the adaptive gain term based on the boundary layer projection operator, and add the adaptive gain term to the Lyapus function stack for storage after projection limiting.

[0131] The Lyapus function stack storing the adaptive gain term is differentiated in time based on the adaptive gain dynamics coefficients. During the differentiation process, the lumped uncertainty term of the motor system is canceled out, and the Lyapus exponent of the adaptive gain dynamics is output.

[0132] When the Lyapus exponent is less than or equal to 0, the dynamic changes of the generalized sliding mode variables under different operating control conditions are strongly coupled with the boundary layer states corresponding to each variable, constructing a control input form for gain leakage scheduling and generating an adaptive gain dynamics online update term.

[0133] It should be noted that by constructing a Lyapus function stack, the system error energy and control gain changes are incorporated into the same stability trade-off framework, clarifying the coupling and fault-tolerance relationship between error and gain. This ensures that adaptive gain adjustment does not compromise the servo control stability of the PMSLM system. The Lyapus function stack quantitatively shapes the influence channels of system uncertainty and external disturbances on stability. Furthermore, by taking the time derivative of the adaptive gain term residing in the Lyapus function stack with the adaptive gain dynamics coefficients, the influence of uncertainty is concentrated and isolated within or isolated from the sliding mode variable-related terms. This can offset the lumped uncertainty terms of the motor system and avoid the misleading influence of uncertainty on the direction of adaptive gain adjustment. Thus, a stability index related only to the sliding mode variable and adaptive gain dynamics is output, clearly identifying individual terms that must be compensated by the control gain, avoiding the conservative design of blindly increasing gain in the adaptive mechanism. When the Lyapus exponent is less than or equal to 0, it indicates that the system state is infinitely close to the sliding surface, and the global trend of gain change slows down. By scheduling the leakage term, the adaptive gain is automatically decayed after the error decreases or enters the boundary layer. Under the premise of ensuring stability, the excessive accumulation of adaptive gain can be suppressed, parameter drift and gain overestimation can be prevented, control input chattering can be reduced, and the dynamic balance and energy efficiency of the system under the adaptive gain dynamic adjustment can be achieved.

[0134] A second aspect of the present invention provides a PMSLM motor delay control system based on adaptive gain dynamics, such as... Figure 2 As shown, the PMSLM motor delay control method based on adaptive gain dynamics described in any one of the claims specifically includes:

[0135] Rectifier module: The rectifier module is used to convert external AC power into a stable DC voltage to provide DC bus power to the inverter module;

[0136] Inverter module: The inverter module is responsible for converting the DC voltage output by the rectifier module into an AC voltage with controllable amplitude, frequency and phase, for driving the PMSLM motor;

[0137] Servo Control Unit: The servo control unit adopts a dual-loop structure with a cascaded position loop and a current loop. The unit includes a time delay controller based on adaptive gain dynamics. Two PI controllers are used in the current loop to generate the control voltage required to drive the PMSLM motor and complete the online calculation of the control algorithm.

[0138] Position encoder module: The position encoder module is used to collect the position and speed information of the PMSLM motor in real time, and obtain the speed information through numerical differentiation or filtering algorithms, and feed it back to the servo control unit;

[0139] PMSLM Motor: The PMSLM motor is responsible for servo action execution under the inverter output voltage, realizing precise position control of the load.

[0140] It should be noted that the servo control unit adopts a position-current dual closed-loop structure. Specifically, the proposed time delay control strategy based on adaptive gain dynamics is used for the position loop and outputs the reference input of the current loop.

[0141] Furthermore, in a preferred embodiment of the present invention, such as Figure 3 As shown, the time delay controller based on adaptive gain dynamics consists of a time delay control circuit and an adaptive gain dynamics control circuit.

[0142] A third aspect of the present invention provides a computer-readable storage medium comprising a PMSLM motor delay control method program based on adaptive gain dynamics, wherein when the PMSLM motor delay control method program based on adaptive gain dynamics is executed by a processor, the steps of any of the PMSLM motor delay control methods based on adaptive gain dynamics described in the present invention are implemented.

[0143] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A PMSLM motor delay control method based on adaptive gain dynamics, characterized in that, Includes the following steps: S01: Establish the dynamic order model of the PMSLM motor position-current dual closed-loop system, define the position tracking error of the PMSLM motor, derive the position tracking error through the dynamic order model, and generate the position error dynamic equation of the PMSLM motor. S02: Construct a linear integral sliding surface, derive the desired position tracking error dynamics on the linear integral sliding surface based on the boundary layer thickness of the dynamically constrained sliding variables, and import the position error dynamics equation into the desired control input to solve according to the equivalent control idea, so as to obtain the equivalent control law for the time delay of the PMSLM motor. S03: Collect historical position and control data of PMSLM motor, construct a time-series dynamic incremental model based on historical position and control data, use time delay estimation method to estimate and filter the lumped uncertainty of motor system online in time-series dynamic incremental model, output time delay estimation term, sum the equivalent control law and time delay estimation term to initially obtain the compensation control law of time delay controller under fixed gain; S04: Set an adjustable adaptive gain term, determine the boundary layer projection operator limit adaptive gain term based on the sliding mode variable boundary layer thickness, and perform time derivative and judgment analysis of the Lyapus function. Design an adaptive gain dynamic to replace the fixed gain of the compensation control law, and automatically adjust the control gain online to adapt to different operating conditions of the PMSLM motor. Specifically, S03 includes the following steps: Collect the current running position status of the PMSLM motor at a preset time node, and extract the historical running position status and corresponding historical control quantity of the previous time adjacent to the preset time node; By combining the current operating position status, the historical operating position status of the previous moment, and historical control variables, the system increment of the PMSLM motor is discretely modeled to construct the time-series dynamic incremental model of the motor. The system lumped uncertainty of the motor is estimated online using a first-order dynamic filter structure in the time-series dynamic incremental model through time delay estimation technology. This yields piecewise continuous incremental estimates of the uncertainty. The equivalent control law then feeds back the incremental estimates from the previous time step to calculate the control disturbance of the dynamic order model after time t0, where the uncertainty has a time delay. This results in the lumped disturbance value of the motor system within the first time period t0, as shown in the formula: ； In the formula: For speed, For acceleration, For the delayed time Shaft reference current, Position status, For equivalent control gain; By combining the lumped disturbance value and the incremental estimate of the uncertainty term, the uncertainty estimate of the PMSLM motor at the current moment is obtained under the condition of time delay estimation. The uncertainty estimate is summed with the equivalent control law of the PMSLM motor time delay to generate the compensation control law of the time delay controller under fixed gain. Specifically, S04 includes the following steps: The error variables of the PMSLM motor under different operating control conditions on the linear integral sliding surface are extracted by the position error tracking log and defined as the generalized sliding mode variable set. Based on the preset adaptive gain dynamic coefficients of the operation control conditions, the boundary layer projection operator is determined according to the boundary layer thickness of the dynamic boundary constraints of the generalized sliding mode variable set. An adaptively adjustable adaptive gain term is set on the linear integral sliding surface. The adaptive gain term is limited by the boundary layer projection operator and injected into the Lyapus function stack. The time derivative and convergence stability are determined by the adaptive gain dynamic coefficients. Based on the Lyapus determination results, an online update term for the adaptive gain dynamics is constructed. By replacing the fixed gain in the compensation control law with an online shape-changing term based on adaptive gain dynamics, a time delay control law for the PMSLM motor based on adaptive gain dynamics is finally obtained. This PMSLM motor time delay control law is used to adapt to the position tracking of the PMSLM motor under different operating control conditions.

2. The PMSLM motor delay control method based on adaptive gain dynamics according to claim 1, characterized in that, S01 specifically includes the following steps: Acquire mover position data, input current data, and one or more uncertainty data of the system lumped together for the PMSLM motor; wherein, the uncertainty data includes parameter uncertainty, thrust fluctuation, nonlinear friction, load disturbance, and unmodeled dynamics; By controlling the dynamic coupling of the mover position data, input current data and various uncertain data through the driving position loop and current loop, a dynamic order model of the PMSLM motor position-current dual closed-loop system is established. Obtain the predetermined position setpoint of the PMSLM motor, and define the position tracking error variable of the motor based on the predetermined position setpoint; The position tracking error variables are imported into the dynamic order model for dynamic derivation, and the position error dynamic equation of the PMSLM motor is output.

3. The PMSLM motor delay control method based on adaptive gain dynamics according to claim 1, characterized in that, S02 specifically includes the following steps: The sliding mode state of the position tracking error of the PMSLM motor from the initial moment to the arrival stage under dynamic time-varying conditions is captured to dynamically constrain the boundary layer thickness of the sliding mode variable, and the sliding mode saturation function is preset based on the boundary layer thickness. Based on position tracking error variables Construct a first-order linear integral sliding surface, use a continuously positioned sliding saturation function on the linear integral sliding surface to replace the discontinuous position tracking sign function, and derive the control law for the sliding variable; In the derivation of the control law, an exponential reaching law for the motor system is set, and the sliding mode is made to satisfy the desired arrival condition of the exponential reaching law. The desired control input for the position tracking error dynamics is obtained, and the formula is: ； In the formula: The second derivative of the position tracking error, The first derivative of the position tracking error. For position tracking error, For the convergence rate coefficient of the exponential reaching law; By introducing the equivalent control concept, the position error dynamic equation is substituted into the desired control input, and finally the equivalent control law for the time delay of the PMSLM motor is obtained.

4. The PMSLM motor delay control method based on adaptive gain dynamics according to claim 1, characterized in that, The method involves using time delay estimation techniques to online estimate the lumped uncertainty of the motor system in a time-series dynamic incremental model using a first-order dynamic filter structure. This yields piecewise continuous incremental estimates of the uncertainty. The equivalent control law then uses feedback to calculate the control disturbance of the dynamic order model after time t0, based on the incremental estimates from the previous time step, to obtain the lumped disturbance value of the motor system within the first time period t0. Specifically, this includes the following steps: By introducing time delay estimation technology and constructing a first-order dynamic filtering structure, the continuous control changes within adjacent sampling times are approximated to the control changes of unknown dynamics and disturbances in the time-series dynamic incremental model, thus enabling online estimation of the system lumped uncertainty of the PMSLM motor. In the uncertain sampling period of a motor position state, the unknown changes slowly. At this time, the differential noise is easily amplified by discrete means. The first-order dynamic filter structure can detect and filter out the chattering caused by differential amplification noise and output relatively smooth incremental estimates of continuous uncertain terms in different segments. The incremental estimate of the continuous uncertainties in the segmented phase of the previous adjacent time is obtained and defined as the preceding incremental estimate. Based on the preceding incremental estimate, the dynamic order model of the PMSLM motor position-current dual closed-loop system is pushed forward to time t0. After pushing forward, the control disturbance caused by the lumped uncertainty of the motor system due to the time delay of the position state is calculated by using the equivalent control law feedback, and the lumped disturbance value of the motor system in the first t0 time period is obtained.

5. The PMSLM motor delay control method based on adaptive gain dynamics according to claim 1, characterized in that, The process involves setting an adaptively adjustable adaptive gain term on the linear integral sliding surface, using a boundary layer projection operator to limit the adaptive gain term, injecting it into the Lyapus function stack, performing time differentiation and convergence stability determination through adaptive gain dynamics coefficients, and constructing an online update term for adaptive gain dynamics based on the Lyapus determination results. This process specifically includes the following steps: Construct a Lyapus function stack, set an adaptive gain term with an adjustable power-law parameter that varies with the position tracking error on the linear integral sliding surface, perform projection limiting on the adaptive gain term based on the boundary layer projection operator, and add the adaptive gain term to the Lyapus function stack for storage after projection limiting. The Lyapus function stack storing the adaptive gain term is differentiated in time based on the adaptive gain dynamics coefficients. During the differentiation process, the lumped uncertainty term of the motor system is canceled out, and the Lyapus exponent of the adaptive gain dynamics is output. When the Lyapus exponent is less than or equal to 0, the dynamic changes of the generalized sliding mode variables under different operating control conditions are strongly coupled with the boundary layer states corresponding to each variable, constructing a control input form for gain leakage scheduling and generating an adaptive gain dynamics online update term.

6. A PMSLM motor delay control system based on adaptive gain dynamics, characterized in that, The PMSLM motor delay control method based on adaptive gain dynamics as described in any one of claims 1-5, the system specifically includes: Rectifier module: The rectifier module is used to convert external AC power into a stable DC voltage to provide DC bus power to the inverter module; Inverter module: The inverter module is responsible for converting the DC voltage output by the rectifier module into an AC voltage with controllable amplitude, frequency and phase, for driving the PMSLM motor; Servo Control Unit: The servo control unit adopts a dual-loop structure with a cascaded position loop and a current loop. The unit includes a time delay controller based on adaptive gain dynamics. Two PI controllers are used in the current loop to generate the control voltage required to drive the PMSLM motor and complete the online calculation of the control algorithm. Position encoder module: The position encoder module is used to collect the position and speed information of the PMSLM motor in real time, and obtain the speed information through numerical differentiation or filtering algorithms, and feed it back to the servo control unit; PMSLM Motor: The PMSLM motor is responsible for servo action execution under the inverter output voltage, realizing precise position control of the load.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a PMSLM motor delay control method program based on adaptive gain dynamics. When the PMSLM motor delay control method program based on adaptive gain dynamics is executed by a processor, it implements the steps of the PMSLM motor delay control method based on adaptive gain dynamics as described in any one of claims 1-5.

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