A high-dynamic steering gear control method and system based on parameter gradual change

Abstract

The application relates to the technical field of high-speed servo control, and discloses a high-dynamic steering engine control method and system based on parameter gradual change, which comprises the following steps: first, initializing the digital controller after power-on, opening the power enable, configuring the zero-state PWM initial control parameters, and constructing the steering engine closed-loop control benchmark; receiving the external steering instruction, combining the communication period and the steering engine control period matching relationship to perform online interpolation on the discrete instruction, converting the coarse-grained discrete instruction into continuous gradual fine control instruction; collecting and filtering the steering engine position feedback signal in real time, and eliminating the sampling noise; calculating the position error according to the fine control instruction and the position feedback, identifying the instruction change rate to obtain the frequency characteristic, adaptively matching the nonlinear proportional control parameters for the high and low frequency instructions, cooperating with the low-pass filter differential parameter dynamic setting, and solving the optimal closed-loop control quantity; finally, converting the control quantity into a PWM driving signal to drive the steering engine. The application improves the response characteristic and control stability of the high-speed missile-borne steering engine.

Description

Technical Field

[0001] This invention relates to the field of control technology for hypersonic missile-borne servo systems, specifically a high-dynamic servo control method and system based on parameter gradual variation. Background Technology

[0002] A servo motor is an electromechanical integrated product and a core component for controlling a missile's attitude, trajectory, and flight stability. Limited by current manufacturing processes, processing levels, and assembly skills, the structural characteristics of servo motors exhibit significant dispersion, resulting in insufficient adaptability of control parameters. However, as new-generation tactical missiles place higher demands on rapid maneuverability, high accuracy, and robustness in complex environments, designing control strategies that balance dynamic response speed and parameter adaptability has become a critical technological bottleneck that urgently needs to be overcome.

[0003] Currently, high-dynamic servo control typically employs a hybrid control method combining traditional segmented fixed-parameter PID control with sliding mode control / fuzzy control and filters. Segmented fixed-parameter PID control requires pre-setting threshold ranges for nonlinear characteristics such as clearance, friction, and load changes. Sudden changes in control parameters during operating condition switching lead to increased output fluctuations and discontinuous control performance with decreased stability. Sliding mode control and fuzzy control involve numerous variables and complex parameter tuning. When dealing with multi-dimensional operating parameters (single unit / section assembly, no-load / loaded / inertial load, 0.1Hz–100Hz sweep frequency command, 0.25°–2° amplitude, three-temperature conditions), the complexity of parameter tuning increases exponentially. Furthermore, to avoid servo elasticity issues, filters need to be added to the control loop based on overall simulation or measured modal and frequency characteristics. However, filters inevitably introduce phase delays, impacting system speed and stability. Summary of the Invention

[0004] In order to overcome the shortcomings of the existing technology, the purpose of this invention is to provide a high dynamic servo control method and system based on parameter gradual change, so as to solve the technical problems of poor high-frequency robustness, discontinuous hybrid control, and inability to simultaneously meet the requirements of stability, accuracy and speed of servo system under different working conditions in the existing high dynamic servo technology.

[0005] This invention is achieved through the following technical solution: In a first aspect, the present invention provides a high-dynamic servo control method based on parameter gradual variation, comprising: The digital controller completes initialization upon power-up, enables the motor power signal, configures the servo motor's zero-state PWM initial control parameters, and establishes the initial reference for the servo motor's closed-loop control. It receives and parses external rudder control commands, and performs online interpolation processing on discrete rudder control commands based on the matching relationship between the system communication cycle and the rudder control cycle, transforming coarse-grained discrete commands into continuously and gradually changing fine control commands. The servo motor position feedback signal is acquired in real time, and the original feedback signal is filtered and preprocessed to remove sampling noise interference, so as to obtain the servo motor position feedback data. The position error is calculated based on the refined gradual control command and the servo position feedback data. The command frequency characteristics are identified by the command change rate. Nonlinear proportional control parameters are adaptively matched for high and low frequency commands. The parameter tuning is completed by combining the low-pass filter differential parameters. The optimal closed-loop control quantity adapted to the current working condition is dynamically solved. The optimal closed-loop control quantity is converted into a PWM drive signal and output to the servo driver. High dynamic servo control is achieved by relying on progressively optimized control commands and adaptive parameters.

[0006] Preferably, the digital controller completes initialization upon power-up, enables the motor power signal, configures the servo's zero-state PWM initial control parameters, and establishes the initial reference for the servo's closed-loop control, including: After the digital controller is powered on and initialized, it outputs a motor power enable signal; based on the bipolar chopper working mode, the PWM control quantity in the servo motor zero-control state is initialized to 50%.

[0007] Preferably, in receiving and parsing external steering commands, and based on the matching relationship between the system communication cycle and the servo control cycle, the discrete steering commands are processed online through interpolation. The system communication cycle is 2ms, and the servo control cycle is 500μs. Linear interpolation is used to complete the command interpolation within a single communication cycle. Using the current communication cycle command DA and the previous communication cycle command DA1 as interpolation references, the gradual control commands corresponding to the four control cycles within the current communication cycle are generated. The specific interpolation formula is as follows: DA[0]=DA DA[1]=((DA-DA1) 0.25F)+DA DA[2]=((DA-DA1) 0.5F)+DA DA[3]=((DA-DA1) 0.75F)+DA Among them, DA is the instruction of the current communication cycle, DA1 is the instruction of the previous communication cycle, and DA[0]~DA[3] are the instructions of each control cycle in the current communication cycle after completing online interpolation.

[0008] Preferably, the servo position feedback signal is acquired in real time, and the original feedback signal is filtered and preprocessed to remove sampling noise interference to obtain servo position feedback data. This includes using mean filtering to filter and reduce noise in the real-time acquired servo position feedback signal, and obtaining stable servo position feedback data after filtering.

[0009] Preferably, in calculating the position error based on refined gradual control commands and servo position feedback data, the specific process includes: The position error for the current control cycle is calculated using the following formula: ek= DA[i]-DF, i=0, 1, 2, 3, Where DA[i] is the interpolated current control cycle command, and DF is the filtered servo position feedback data; The instruction differential method is used to identify instruction frequency levels and calculate the instruction rate of change. The calculation formula is as follows: Δs=|DA[i]-DA1[i-1]| / Δt, i=1, 2, 3 Where Δt is the duration of the control cycle, and DA1[i-1] is the control command of the previous control cycle.

[0010] Furthermore, the method of identifying command frequency characteristics through command change rate and adaptively matching nonlinear proportional control parameters for high and low frequency commands is as follows: When Δs>Δs max When the command is identified as a high-frequency instruction, the control gain is reduced to improve the robustness of the system under high-frequency operating conditions. When Δs≤Δs max When the position error is within the preset range [ea, eb], the proportional parameter is calculated using a quadratic function of the position error, as shown in the following formula:

[0011] Where a1 < 0, a2 < 0, a3 > 0, if the position error ek is outside the interval [ea, eb], Kp takes the value Kpmin, and the calculated Kp is subjected to amplitude limiting processing.

[0012] Preferably, the configuration of the differential control parameters and the filtering method are as follows: The differential coefficient Kd and the proportional coefficient Kp satisfy a linear relationship: Kd=K×Kp, and the coefficient K takes values ​​from 5 to 10. The differential term is subjected to low-pass filtering, and the filtering iteration formula is as follows:

[0013] in, ;ek is the position error of the current control cycle, ek1 is the position error of the previous control cycle, coefficient a is the filter coefficient, and the value is Tf / (Ts+Tf), where Tf is the filter time constant and Ts is the sampling time.

[0014] Preferably, parameter tuning is completed by combining the differential parameters of the low-pass filter, and the optimal closed-loop control quantity adapted to the current operating condition is dynamically solved. The calculation formula is as follows:

[0015] in, ; The scaling factor for adaptive configuration; This represents the current position error. This is the differential term after filtering.

[0016] Preferably, the optimal closed-loop control quantity is converted into a PWM drive signal and output to the servo driver, relying on progressively optimized control commands and adaptive parameters, including: The current communication cycle command DA, the previous communication cycle command DA1, and the servo position feedback DF are sampled and held. The command online interpolation, command frequency identification, high and low frequency command differentiation, and nonlinear control parameter calculation are completed in sequence. Finally, a PWM signal is generated based on the continuously changing control parameters and updated to the driver to complete the high-precision smooth closed-loop control of the servo.

[0017] Secondly, the present invention also provides a high-dynamic servo control system based on parameter gradual variation, comprising: The initialization module is used to complete the initialization of the digital controller upon power-up, enable the motor power signal, configure the servo motor zero-state PWM initial control parameters, and establish the initial reference for the servo motor closed-loop control. The command interpolation module is used to receive and parse external rudder control commands. Based on the matching relationship between the system communication cycle and the rudder control cycle, it performs online interpolation processing on discrete rudder control commands, transforming coarse-grained discrete commands into continuously and gradually changing fine control commands. The feedback filtering module is used to acquire the servo position feedback signal in real time, and to filter and preprocess the original feedback signal to remove sampling noise interference and obtain the servo position feedback data. The parameter calculation module is used to calculate the position error based on the refined gradual control command and the servo position feedback data. It identifies the command frequency characteristics by the command change rate, adaptively matches nonlinear proportional control parameters for high and low frequency commands, and completes parameter tuning by combining low-pass filter differential parameters. It dynamically solves the optimal closed-loop control quantity that is suitable for the current working condition. The drive output module is used to convert the optimal closed-loop control quantity into a PWM drive signal and output it to the servo driver. It completes high-dynamic servo control by relying on progressively optimized control commands and adaptive parameters.

[0018] Compared with the prior art, the present invention has the following beneficial technical effects: This invention provides a high-dynamic servo control method based on parameter gradual variation. Through a progressive control process involving controller initialization benchmark establishment, online interpolation and smoothing of discrete servo commands, filtering and noise reduction of position feedback signals, adaptive parameter tuning based on command frequency identification, and closed-loop drive output, this method effectively solves the industry technical challenges of poor high-frequency robustness, discontinuous hybrid control, and difficulty in simultaneously ensuring stability, accuracy, and speed of the servo system under different operating conditions in existing high-dynamic servos. This control method relies on command interpolation to achieve continuous and gradual variation of control commands, avoiding the impact and adverse effects of abrupt changes in traditional control quantities on the servo system. Simultaneously, it adaptively matches nonlinear proportional control parameters according to the high and low frequency characteristics of the commands, and optimizes differential parameters using low-pass filtering. This achieves precise control logic that matches large control quantities with small errors and small control quantities with large errors, significantly improving system operational stability while ensuring rapid servo response and high-precision control performance. Based on actual frequency response test results, the optimization effect of this solution is significant. In the low-frequency band, the amplitude at 0.159Hz is increased from -0.212dB to -0.115dB, greatly reducing the zero-value deviation and effectively improving the servo control accuracy. In the mid-frequency band, the phase shift at 12.738Hz is increased from -33.422° to -25.984°, significantly reducing the servo response delay and improving the dynamic response speed. In the high-frequency band, the amplitude attenuation at 47.746Hz is increased from -9.778dB to -14.779dB, greatly enhancing the servo's high-frequency anti-interference capability and operational robustness. Overall, the dynamic performance of the servo is improved by more than 10%, comprehensively addressing the core requirements of stability, accuracy, and speed of the servo control system, and effectively improving the overall dynamic performance and operational reliability of the high-dynamic servo. Attached Figure Description

[0019] Figure 1 This is a flowchart of a high-dynamic servo control method based on parameter gradual variation in an embodiment of the present invention; Figure 2 This is a block diagram illustrating the principle of the servo control system in an embodiment of the present invention. Figure 3 This is a block diagram of the multidimensional state model in an embodiment of the present invention; Figure 4 This is a schematic diagram of a high-dynamic servo control system based on parameter gradual variation in an embodiment of the present invention; In the diagram: 1. Initialization module; 2. Instruction interpolation module; 3. Feedback filtering module; 4. Parameter calculation module; 5. Drive output module. Detailed Implementation

[0020] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0021] The purpose of this invention is to provide a high-dynamic servo control method and system based on parameter gradual variation, so as to solve the technical problems of poor high-frequency robustness, discontinuous hybrid control, and inability to simultaneously meet the requirements of stability, accuracy and speed of servo system under different working conditions in the existing high-dynamic servo technology.

[0022] The present invention will now be described in further detail with reference to the accompanying drawings: Example 1 In one embodiment of the present invention, a high-dynamic servo control method based on parameter gradual variation is provided, and the overall control system architecture is as follows: Figure 2 As shown, the upper-level terminal is the control computer, and the control driver integrates frequency identification, a multi-dimensional state model, control algorithms, and driver units. Inside the servo motor, a servo motor, a reducer, and sensors are arranged sequentially. After the sensors collect the actual position of the control surface, they output a position feedback signal. This signal is corrected by a performance compensation stage and then sent to the closed-loop summing node. The error is calculated by comparing this signal with the target command issued by the control computer, and then sent to the control driver to complete the closed-loop calculation. The signal processing logic within the multi-dimensional state model is referenced... Figure 3 The control commands and position feedback are first sent to the data sampling and holding module, which splits them into two processing links. The control commands undergo online interpolation and frequency identification processing, while the error generated by the position feedback is processed by nonlinear continuous function prediction. The two results are input into the decision module, which outputs adaptively adjusted control parameters Kp and Kd to support subsequent closed-loop calculations. The specific execution steps of this method are as follows: Step 1: The digital controller is powered on and initialized, the motor power enable signal is turned on, and the initial control parameters of the servo motor in zero state PWM are configured to establish the initial reference for the servo motor closed-loop control. Specifically, upon power-up, the digital controller initializes the underlying registers and peripheral resources, and enables the motor power signal. Adapting to the bipolar chopper drive requirements, it initializes the PWM control quantity to 50% when the servo is in zero-control state, establishing the initial baseline for the entire servo closed-loop control. During the power-up initialization phase, the parameters of the drive channel, sampling channel, timer, and PWM output module are configured to ensure the subsequent command transmission and reception, position sampling, and PWM output hardware paths function correctly. The initial PWM with a 50% duty cycle ensures that the average voltage across the motor windings is zero, allowing the servo to stably maintain a zero position and preventing erroneous deflection of the control surface at power-up.

[0023] Step 2: Receive and parse external steering commands. Based on the matching relationship between the system communication cycle and the servo control cycle, perform online interpolation processing on the discrete steering commands to transform coarse-grained discrete commands into continuously and gradually changing fine-grained control commands. Specifically, the digital controller receives steering commands from the control computer, parses the commands according to a preset communication protocol, and distributes them to the corresponding control channels. The system's communication cycle is set to 2ms, and the servo's internal control cycle is set to 500μs. A single communication cycle contains four control cycles. Linear interpolation is used for online interpolation, decomposing the coarse-grained discrete commands output from the communication cycle into continuously varying commands at the control cycle granularity, eliminating servo jitter caused by abrupt changes in commands. The interpolation uses the current communication cycle command DA and the previous communication cycle command DA1 as references, resulting in four periodic commands: DA[0]=DA DA[1]=((DA-DA1) 0.25F)+DA DA[2]=((DA-DA1) 0.5F)+DA DA[3]=((DA-DA1) 0.75F)+DA Among them, DA is the instruction of the current communication cycle, DA1 is the instruction of the previous communication cycle, and DA[0]~DA[3] are the instructions of each control cycle in the current communication cycle after completing online interpolation.

[0024] The interpolated commands change more smoothly, reducing the impact on the servo's mechanical structure and improving dynamic tracking performance. The interpolated DA and DA1 signals are sent to the frequency identification module for subsequent command frequency feature extraction.

[0025] Step 3: Real-time acquisition of servo position feedback signals, and filtering and preprocessing of the raw feedback signals to remove sampling noise interference, thereby obtaining servo position feedback data; Specifically, the sensor acquires the current position of the control surface in real time to obtain the raw position feedback signal. After being transmitted to the control driver, the raw sampled data is denoised using a mean filtering algorithm to remove sampling glitches introduced by circuit noise and mechanical vibration, and outputs a stable and reliable position feedback value DF. The filtered position feedback is sent to the summing node and target command to calculate the position error, and also to the data sampling and holding module of the multidimensional state model to provide input data for the nonlinear continuous function prediction module.

[0026] Step 4: Calculate the position error based on the refined gradual control command and the servo position feedback data, identify the command frequency characteristics by the command change rate, adaptively match the nonlinear proportional control parameters for high and low frequency commands, complete the parameter tuning by combining the low-pass filter differential parameters, and dynamically solve the optimal closed-loop control quantity that is suitable for the current working condition. Specifically, the position error of the current control cycle is calculated using the following formula: ek=DA[i]-DF, i=0, 1, 2, 3, Where DA[i] is the interpolated current control cycle command, and DF is the filtered servo position feedback data; The instruction differential method is used to identify instruction frequency levels and calculate the instruction rate of change. The calculation formula is as follows: Δs=|DA[i]-DA1[i-1]| / Δt, i=1, 2, 3 Where Δt is the duration of the control cycle, and DA1[i-1] is the control command of the previous control cycle.

[0027] Among them, Δs and the preset threshold Δs max Compare and complete the proportional coefficient (k_p) tuning: When Δs>Δs max When the command is identified as a high-frequency instruction, the control gain is reduced to improve the robustness of the system under high-frequency operating conditions. When Δs≤Δs max When the position error is within the preset range [ea, eb], the proportional parameter is calculated using a quadratic function of the position error, as shown in the following formula:

[0028] Where a1 < 0, a2 < 0, a3 > 0, if the position error ek is outside the interval [ea, eb], Kp takes the value Kpmin, and the calculated Kp is subjected to amplitude limiting processing.

[0029] Specifically, the configuration of the differential control parameters and the filtering method are as follows: The differential coefficient Kd and the proportional coefficient Kp satisfy a linear relationship: Kd=K×Kp, and the coefficient K takes values ​​from 5 to 10. The differential term is subjected to low-pass filtering, and the filtering iteration formula is as follows:

[0030] in, ;ek is the position error of the current control cycle, ek1 is the position error of the previous control cycle, coefficient a is the filter coefficient, and the value is Tf / (Ts+Tf), where Tf is the filter time constant and Ts is the sampling time.

[0031] The parameters are tuned by combining the differential parameters of the low-pass filter, and the optimal closed-loop control quantity adapted to the current operating condition is dynamically solved. The calculation formula is as follows:

[0032] in, ; The scaling factor for adaptive configuration; This represents the current position error. This is the differential term after filtering.

[0033] In this stage, two processing links within the multidimensional state model operate synchronously: the command link outputs frequency characteristics through online interpolation and frequency identification, while the error link predicts the error evolution trend through nonlinear continuous functions. Both types of information are jointly fed into the decision module output (Kp, Kd) to complete the entire parameter tuning logic. This parameter tuning logic can configure large control quantities for small errors, ensuring the servo motor's response speed and control accuracy; and configure small control quantities for large errors, ensuring the servo motor's operational stability. At the same time, the parameters change continuously and gradually, avoiding sudden changes in control quantities that could impact the mechanical structure.

[0034] Step 5: Convert the optimal closed-loop control quantity into a PWM drive signal and output it to the servo driver. Based on the progressively optimized control command and adaptive parameters, complete the high-dynamic servo control.

[0035] Specifically, the calculated control quantity is converted into a PWM signal with a corresponding duty cycle and sent to the power driver. The driver drives the servo motor to rotate. The motion is reduced in speed and increased in torque by the reducer and then transmitted to the control surface. The sensor collects the position of the control surface in real time. The position signal is corrected by the performance compensation stage and then fed back to the input to form a closed loop.

[0036] In summary, the high-dynamic servo control method based on parameter gradual variation provided in this embodiment significantly improves the dynamic performance and reliability of the servo. The high-dynamic servo is mainly characterized by the 1° frequency response test results. In the low-frequency band (0.159Hz), the closer the amplitude is to 0, the higher the accuracy, improving from -0.212dB to -0.115dB. In the mid-frequency band (12.738Hz), the larger the phase shift, the smaller the servo response delay, improving from -33.422° to -25.984°. In the high-frequency band (47.746Hz), the greater the amplitude attenuation, the stronger the anti-interference capability, improving from -9.778dB to -14.779dB. The overall dynamic performance of the product is improved by more than 10%.

[0037] This control method not only achieves small error and large control quantity to ensure the speed and high precision of the servo motor; large error and small control quantity to ensure the stability of the servo motor; and continuous and gradual change of control parameters to ensure continuous action of the servo motor and reduce the adverse effects of sudden changes in control quantity on the servo motor system.

[0038] Example 2 This embodiment also provides a high-dynamic servo control system based on parameter gradual variation, used to run the control method described in Embodiment 1. The system hardware connection is as follows: Figure 2 For details of the internal multidimensional state model, please refer to... Figure 3 The system comprises five main functional modules, and the functions and interaction logic of each module are as follows: Initialization module 1 is used to complete the initialization of the digital controller upon power-up, enable the motor power signal, configure the servo motor zero-state PWM initial control parameters, and establish the initial reference for the servo motor closed-loop control. Command interpolation module 2 is used to receive and parse external rudder control commands. Based on the matching relationship between the system communication cycle and the rudder control cycle, it performs online interpolation processing on discrete rudder control commands, transforming coarse-grained discrete commands into continuously and gradually changing fine control commands. Feedback filtering module 3 is used to acquire servo position feedback signals in real time, and to filter and preprocess the original feedback signals to remove sampling noise interference and obtain servo position feedback data. Parameter calculation module 4 is used to calculate the position error based on the refined gradual control command and the servo position feedback data. It identifies the command frequency characteristics by the command change rate, adaptively matches nonlinear proportional control parameters for high and low frequency commands, and completes parameter tuning by combining low-pass filter differential parameters. It dynamically solves the optimal closed-loop control quantity that is suitable for the current working condition. The drive output module 5 is used to convert the optimal closed-loop control quantity into a PWM drive signal and output it to the servo driver. It completes high-dynamic servo control by relying on progressively optimized control commands and adaptive parameters.

[0039] The entire system relies on a multi-dimensional state model to achieve smooth command processing, frequency band identification, and adaptive adjustment of nonlinear parameters. It solves the problems of weak robustness of traditional servo motors in high-frequency operating conditions, structural impact from sudden changes in control quantities, and difficulty in achieving stability, accuracy, and speed under different operating conditions. It forms a hardware and software correspondence with the aforementioned method implementation, and can be independently deployed to build servo motor control hardware products.

[0040] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A high-dynamic servo motor control method based on parameter gradual variation, characterized in that, include: The digital controller completes initialization upon power-up, enables the motor power signal, configures the servo motor's zero-state PWM initial control parameters, and establishes the initial reference for the servo motor's closed-loop control. It receives and parses external rudder control commands, and performs online interpolation processing on discrete rudder control commands based on the matching relationship between the system communication cycle and the rudder control cycle, transforming coarse-grained discrete commands into continuously and gradually changing fine control commands. The servo motor position feedback signal is acquired in real time, and the original feedback signal is filtered and preprocessed to remove sampling noise interference, so as to obtain the servo motor position feedback data. The position error is calculated based on the refined gradual control command and the servo position feedback data. The command frequency characteristics are identified by the command change rate. Nonlinear proportional control parameters are adaptively matched for high and low frequency commands. The parameter tuning is completed by combining the low-pass filter differential parameters. The optimal closed-loop control quantity adapted to the current working condition is dynamically solved. The optimal closed-loop control quantity is converted into a PWM drive signal and output to the servo driver. High dynamic servo control is achieved by relying on progressively optimized control commands and adaptive parameters.

2. The high-dynamic servo control method based on parameter gradual variation according to claim 1, characterized in that, The digital controller completes initialization upon power-up, enables the motor power signal, configures the servo's zero-state PWM initial control parameters, and establishes the initial reference for the servo's closed-loop control, including: After the digital controller is powered on and initialized, it outputs a motor power enable signal; based on the bipolar chopper working mode, the PWM control quantity in the servo motor zero-control state is initialized to 50%.

3. The high-dynamic servo control method based on parameter gradual variation according to claim 1, characterized in that, The system receives and parses external steering commands. Based on the matching relationship between the system communication cycle and the servo control cycle, it performs online interpolation processing on discrete steering commands. The system communication cycle is 2ms, and the servo control cycle is 500μs. Linear interpolation is used to complete the command interpolation within a single communication cycle. Using the current communication cycle command DA and the previous communication cycle command DA1 as interpolation references, it generates gradual control commands corresponding to the four control cycles within the current communication cycle. The specific interpolation formula is as follows: DA[0]=DA YES[1]=((YES-YES1) 0.25F)+YES YES[2]=((YES-YES1) 0.5F)+YES DA[3]=((DA-DA1) 0.75F)+DA Among them, DA is the instruction of the current communication cycle, DA1 is the instruction of the previous communication cycle, and DA[0]~DA[3] are the instructions of each control cycle in the current communication cycle after completing online interpolation.

4. The high-dynamic servo control method based on parameter gradual variation according to claim 1, characterized in that, The system acquires servo position feedback signals in real time and performs filtering preprocessing on the raw feedback signals to remove sampling noise interference, thereby obtaining servo position feedback data. This includes using mean filtering to filter and reduce noise in the real-time acquired servo position feedback signals, resulting in stable servo position feedback data.

5. The high-dynamic servo control method based on parameter gradual variation according to claim 1, characterized in that, The specific process for calculating position error based on refined gradual control commands and servo position feedback data includes: The position error for the current control cycle is calculated using the following formula: ek= DA[i]-DF, i=0, 1, 2, 3, Where DA[i] is the interpolated current control cycle command, and DF is the filtered servo position feedback data; The instruction differential method is used to identify instruction frequency levels and calculate the instruction rate of change. The calculation formula is as follows: Δs=|DA[i]-DA1[i-1]| / Δt, i=1, 2, 3 Where Δt is the duration of the control cycle, and DA1[i-1] is the control command of the previous control cycle.

6. The high-dynamic servo control method based on parameter gradual variation according to claim 5, characterized in that, The method of identifying command frequency characteristics by analyzing the command change rate and adaptively matching nonlinear proportional control parameters for high and low frequency commands is as follows: When Δs>Δs max When the command is identified as a high-frequency instruction, the control gain is reduced to improve the robustness of the system under high-frequency operating conditions. When Δs≤Δs max When the position error is within the preset range [ea, eb], the proportional parameter is calculated using a quadratic function of the position error, as shown in the following formula: Where a1 < 0, a2 < 0, a3 > 0, if the position error ek is outside the interval [ea, eb], Kp takes the value Kpmin, and the calculated Kp is subjected to amplitude limiting processing.

7. The high-dynamic servo control method based on parameter gradual variation according to claim 1, characterized in that, The specific configuration of the differential control parameters and the filtering method are as follows: The differential coefficient Kd and the proportional coefficient Kp satisfy a linear relationship: Kd=K×Kp, and the coefficient K takes values ​​from 5 to 10. The differential term is subjected to low-pass filtering, and the filtering iteration formula is as follows: in, ;ek is the position error of the current control cycle, ek1 is the position error of the previous control cycle, coefficient a is the filter coefficient, and the value is Tf / (Ts+Tf), where Tf is the filter time constant and Ts is the sampling time.

8. The high-dynamic servo control method based on parameter gradual variation according to claim 1, characterized in that, The parameters are tuned by combining the differential parameters of the low-pass filter, and the optimal closed-loop control quantity adapted to the current operating condition is dynamically solved. The calculation formula is as follows: in, ; The scaling factor for adaptive configuration; This represents the current position error. This is the differential term after filtering.

9. The high-dynamic servo control method based on parameter gradual variation according to claim 1, characterized in that, The optimal closed-loop control quantity is converted into a PWM drive signal and output to the servo driver, relying on progressively optimized control commands and adaptive parameters, including: The current communication cycle command DA, the previous communication cycle command DA1, and the servo position feedback DF are sampled and held. The command online interpolation, command frequency identification, high and low frequency command differentiation, and nonlinear control parameter calculation are completed in sequence. Finally, a PWM signal is generated based on the continuously changing control parameters and updated to the driver to complete the high-precision smooth closed-loop control of the servo.

10. A high-dynamic servo control system based on parameter gradual variation, characterized in that, include: The initialization module is used to complete the initialization of the digital controller upon power-up, enable the motor power signal, configure the servo motor zero-state PWM initial control parameters, and establish the initial reference for the servo motor closed-loop control. The command interpolation module is used to receive and parse external rudder control commands. Based on the matching relationship between the system communication cycle and the rudder control cycle, it performs online interpolation processing on discrete rudder control commands, transforming coarse-grained discrete commands into continuously and gradually changing fine control commands. The feedback filtering module is used to acquire the servo position feedback signal in real time, and to filter and preprocess the original feedback signal to remove sampling noise interference and obtain the servo position feedback data. The parameter calculation module is used to calculate the position error based on the refined gradual control command and the servo position feedback data. It identifies the command frequency characteristics by the command change rate, adaptively matches nonlinear proportional control parameters for high and low frequency commands, and completes parameter tuning by combining low-pass filter differential parameters. It dynamically solves the optimal closed-loop control quantity that is suitable for the current working condition. The drive output module is used to convert the optimal closed-loop control quantity into a PWM drive signal and output it to the servo driver. It completes high-dynamic servo control by relying on progressively optimized control commands and adaptive parameters.

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