Accurate steering engine control method and system based on optimization control algorithm

Abstract

The invention discloses a steering engine accurate control method and system based on an optimization control algorithm, and the system comprises a data collection module which collects key working parameters of a steering engine in real time through a plurality of sensors, and carries out the data processing, thereby forming a state vector; the self-adaptive filtering module is used for carrying out self-adaptive filtering fusion processing on the data acquired by the sensor and dynamically estimating the attitude angle and the corresponding angular velocity of the aircraft; the sliding mode control module constructs a sliding mode surface and a control rate of a steering engine system according to the dynamic error of the system, calculates control input of each axis of the aircraft according to the attitude angle real-time state error, and generates a sliding mode control signal; the driving execution module is used for converting the control signal into a voltage or PWM signal and inputting the voltage or PWM signal into a steering engine, so as to change thrust and realize adjustment and control of a posture or a position; and the real-time feedback module monitors the current state of the steering engine in real time to realize dynamic correction and real-time optimization. The adaptive filtering and sliding mode control method provided by the invention is higher in control precision, quicker in system response and higher in adaptability to complex environments, and can effectively improve the application performance and stability of a steering engine execution system in the fields of industrial automation, aerospace, intelligent manufacturing and the like.

Description

Technical Field

[0001] The present invention relates to the field of automatic control technology, and specifically to a method and system for precise control of a servo, in particular a method and system for precise control of a servo based on an optimization control algorithm, which is suitable for application scenarios such as robot joint control, drone attitude adjustment, and automation equipment actuator control that have high requirements on the response speed and stability of the servo. Background Art

[0002] With the continuous advancement of intelligent control technology and high-precision actuators, modern unmanned systems, robotics, and automated equipment are rapidly developing towards high precision and robustness. As a key actuator for angle control and attitude adjustment, the control performance of servos directly determines the response speed, stability, and accuracy of the entire system. However, in complex real-world operating environments, servo systems are often subject to a variety of non-ideal factors such as friction interference, parameter uncertainty, external disturbances, and sensor noise, which severely restrict the system's control accuracy and stable operation.

[0003] Traditional control strategies, such as PID control, rely on precise system models and fixed parameter adjustment methods. Consequently, they suffer from insufficient adjustment accuracy, poor robustness, and weak dynamic response capabilities when faced with complex situations such as strong interference, nonlinearity, and parameter variations. Although a variety of intelligent control methods (such as fuzzy control and neural network control) have emerged in recent years, they often require large amounts of training data or rely on empirical rules, resulting in complex system designs and poor real-time performance, limiting their practical application in embedded servo control systems.

[0004] To improve the dynamic response and stability of servos in uncertain environments, sliding mode control (SMC) has gradually attracted widespread attention in the research and engineering communities. As a robust control method with clear control laws and applicability to nonlinear systems, sliding mode control has a natural ability to suppress system uncertainties and external disturbances. In particular, in servo control, sliding mode control can effectively ensure rapid convergence of system states along the sliding mode surface and stable operation by designing appropriate switching surfaces and reaching laws, thereby improving the servo's response speed and attitude control accuracy.

[0005] In addition, the sensors in the servo control system are easily interfered by noise when collecting status information such as angle and angular velocity, resulting in inaccurate system status estimation. Summary of the Invention

[0006] To address the aforementioned issues with the existing technology, the present invention introduces an adaptive filtering algorithm into the system. This algorithm can suppress measurement noise in real time during dynamic flight or changing environments, improving state estimation accuracy and providing more reliable state input for the sliding mode controller. Furthermore, the present method leverages the robustness of sliding mode control and the high-precision state estimation capabilities of adaptive filtering to significantly enhance the anti-interference performance, dynamic response capability, and control accuracy of the servo control. This approach is particularly suitable for unmanned systems and intelligent equipment applications in complex environments, where high demands are placed on servo execution accuracy and robustness.

[0007] In the present invention, the steering gear control system includes a data acquisition module, an adaptive filtering module, a sliding mode control module, a drive execution module, and a real-time feedback module. The system acquires the steering gear's state information through acquisition sensors (such as angle sensors, current sensors, and gyroscopes), and uses an adaptive filtering algorithm for real-time filtering and state estimation. Subsequently, through sliding mode surface construction, reaching law design, and control variable calculation, it dynamically generates control signals to accurately drive the steering gear to perform predetermined actions. The system also has a feedback correction function that can perform closed-loop adjustments based on the feedback status, further improving response sensitivity and anti-interference capabilities.

[0008] The technical solutions adopted by the present invention to solve the technical problems are as follows:

[0009] The precise control method of the servo based on the optimization control algorithm includes:

[0010] Step 1: Use multiple sensors to collect key operating parameters of the servo in real time, process the data and form a state vector as the basic input data;

[0011] Step 2: Use an adaptive filtering algorithm to fuse the data collected by the sensors, adjust the model parameters online based on the input-output state vector data at each moment, filter out high-frequency noise, and dynamically estimate the state information of the aircraft: attitude angle and corresponding angular velocity;

[0012] Step 3: Construct the sliding surface of the servo system based on the system dynamic error, and combine it with the filtered state information to measure the deviation between the actual state of the servo and the desired target; construct the approach law of the sliding surface to generate the control law model to ensure that the system state quickly and stably approaches the sliding surface and slides along it to reach the desired state; according to the real-time state error of the attitude angle e i (t) Calculate the control input of each axis of the aircraft and generate the final sliding mode control signal;

[0013] Step 4: Convert the control signal into a voltage or PWM signal and input it into the servo to change the thrust to achieve attitude or position adjustment and control;

[0014] Step 5: Monitor the current state of the servo in real time and compare it with the desired state to form a closed-loop control structure to achieve dynamic correction and real-time optimization.

[0015] The key operating parameters include angle, speed, and current data; the multiple sensors include but are not limited to an inertial measurement unit, a gyroscope, an accelerometer, a magnetometer, and a GPS sensor.

[0016] The data processing of the key operating parameters includes:

[0017] Use timestamp correction method to synchronize data clock;

[0018] A digital filter is used to remove high-frequency noise from the sensor signal, and a sliding window anomaly detection method is used to remove obvious abnormal data points.

[0019] The adaptive filtering algorithm is an extended Kalman filter or a weighted complementary filter method; the model parameters are adjusted online based on the input-output state vector data at each moment, high-frequency noise is filtered out, and the attitude angle and corresponding angular velocity of the aircraft are dynamically estimated, including:

[0020] Predict the current state based on the previous state and motion model, and predict the covariance matrix;

[0021] Use the current sensor observations to calculate the Kalman gain, update the state estimate and covariance matrix, and correct the prediction error;

[0022] Output the current attitude angle error and angular velocity error, and update the state vector.

[0023] The sliding surface is defined as:

[0024]

[0025] Among them, e i (t) = θ i (t)-θ i,d (t) is the error of the i-th attitude angle, λ i >0 is the adjustment parameter, which determines the error convergence speed and system stability;

[0026] The sliding mode control law is:

[0027]

[0028] Among them, u eq,i (t) is the equivalent control used to compensate for the known dynamics of the system; k i >0 is the control gain, which ensures that the system reaches the sliding surface; sat(·) is the saturation function, which limits the control signal to prevent high-frequency chattering; φ iis the boundary layer width, which adjusts the smoothness of the sliding mode control;

[0029] According to the attitude angle real-time state error e i (t) Calculate the control input of each axis and generate the final sliding mode control signal: including the speed adjustment amount for each motor.

[0030] The real-time monitoring of the current state of the servo and comparison of it with the desired state form a closed-loop control structure to achieve dynamic correction and real-time optimization, including:

[0031] Continuously receive the latest data from the gyroscope, accelerometer, and magnetometer, and fuse them through an adaptive filtering algorithm to obtain the latest state estimates of attitude angle and angular velocity;

[0032] By monitoring the system error and dynamic response, the sliding surface parameter λ is adaptively adjusted. i , control gain k i and the boundary layer width φ i , which enables the sliding mode controller to adjust the sliding mode control signal in real time according to changes in the flight environment, ensuring control stability and response speed, and preventing excessive vibration or control hysteresis.

[0033] The servo precision control system based on the optimized control algorithm includes:

[0034] The data acquisition module receives key operating parameters of the servo collected in real time by various sensors, processes the data to form a state vector, which serves as the basic input data of the control system;

[0035] The adaptive filtering module is used to perform adaptive filtering and fusion processing on the data collected by the sensors, adjust the model parameters online according to the input-output data at each moment, filter out high-frequency noise, and dynamically estimate the state information of the aircraft: attitude angle and corresponding angular velocity;

[0036] The sliding mode control module constructs the sliding mode surface of the servo system based on the system dynamic error, and combines the filtered state information to measure the deviation between the actual state of the servo and the desired target; constructs the approach law of the sliding mode surface to generate the control law to ensure that the system state quickly and stably approaches the sliding mode surface and slides along it to reach the desired state; according to the real-time state error of the attitude angle e i (t) Calculate the control input of each axis of the aircraft and generate the final sliding mode control signal;

[0037] The drive execution module is used to convert the control signal into a voltage or PWM signal and input it into the servo, changing the thrust to achieve attitude or position adjustment and control;

[0038] The real-time feedback module monitors the current state of the servo in real time and compares it with the desired state to form a closed-loop control structure, achieving dynamic correction and real-time optimization.

[0039] The high-precision steering gear control system provided by the present invention has the following advantages:

[0040] (1) Strong precise control capability: By introducing the sliding surface control theory, it effectively solves the interference problems such as nonlinearity, dead zone, friction, etc. in the servo system and improves the response accuracy.

[0041] (2) Superior robustness: The sliding mode controller is naturally robust to parameter uncertainty and external interference, and can correct state errors in real time when combined with an adaptive filtering algorithm.

[0042] (3) Strong dynamic adaptability: The adaptive filtering module can adjust the filter weight according to environmental changes, realizing real-time modeling of complex flight states or mechanical motion processes.

[0043] (4) Fast system response speed: Sliding mode control has a fast approach speed and can quickly make precise control under conditions such as starting and braking, which is suitable for high dynamic task requirements.

[0044] (5) Wide adaptability: This control solution can be applied to various types of servos and actuators, and is suitable for various intelligent devices such as drones, robots, and robotic arms, with good engineering versatility and scalability.

[0045] In summary, compared with the existing traditional PID-based servo control scheme, the adaptive filtering + sliding mode control method proposed in this invention not only has higher control accuracy and faster system response, but also has stronger adaptability to complex environments. It can effectively improve the application performance and stability of the servo execution system in industrial automation, aerospace, intelligent manufacturing and other fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 This is a flow chart of the filtering algorithm of the present invention.

[0047] Figure 2 This is a flow chart of the steering gear control of the present invention. DETAILED DESCRIPTION

[0048] To make the above-mentioned objects, features, and advantages of the present invention more readily apparent, the specific implementation methods of the present invention are described in detail below with reference to the accompanying drawings. The following description sets forth many specific details to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways than those described herein, and those skilled in the art may make similar modifications without departing from the scope of the invention. Therefore, the present invention is not limited to the specific implementation methods disclosed below.

[0049] Unless otherwise defined, all technical and scientific terms used herein have the same meanings as commonly understood by those skilled in the art to which this invention belongs. The terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The present invention will be further described in detail below with reference to the accompanying drawings and examples.

[0050] The present invention relates to a four-rotor UAV flight control system based on a sliding mode control algorithm. The overall system architecture is as follows Figure 2 As shown in the figure, the system includes a system initialization module, a multi-sensor data acquisition module, a state estimation module, a sliding mode control module, a servo drive module, and a state feedback and adaptive adjustment module. The system accurately estimates the flight state through multi-sensor data fusion and designs a robust controller based on the sliding mode control algorithm to ensure stable and efficient response of the UAV in different flight states.

[0051] The workflow of the present invention specifically includes the following steps:

[0052] S10: System initialization

[0053] The quadrotor UAV flight control system first initializes, setting flight control parameters and determining the UAV's initial attitude angle and angular velocity. The gyroscope and accelerometer in the inertial measurement unit (IMU) provide initial measurement data. The system corrects the gyroscope bias and statically calibrates the accelerometer data to ensure accurate initial states. This initial state serves as the basis for subsequent state estimation and control.

[0054] S20: Multi-sensor data acquisition

[0055] The system collects data from sensors such as gyroscopes, accelerometers, magnetometers, and GPS in real time. To improve data quality, preprocessing methods such as recursive least squares (RLS) filters, low-pass filters, and Kalman filters are used to reduce noise, eliminate data anomalies, and ensure the accuracy and stability of subsequent estimates. Sensor data is time-synchronized to ensure consistent timing.

[0056] S30: Flight state estimation

[0057] The Extended Kalman Filter (EKF) or weighted complementary filtering method is used to fuse sensor data and dynamically estimate the aircraft's attitude angles (pitch, roll, and yaw) and their corresponding angular velocities. The filter continuously corrects the state estimate, eliminating the effects of gyroscope drift and accelerometer noise, and obtains highly accurate state information, providing reliable input for the control algorithm.

[0058] S40: Sliding Mode Controller Design

[0059] A sliding mode controller is designed based on real-time estimated attitude angle and angular velocity errors. A sliding surface is defined to express the error state of the control objective, and a switching control law is designed to achieve rapid convergence of the system state. Through switching control, the sliding mode controller ensures strong robustness to parameter uncertainty and external disturbances, effectively suppressing oscillations and disturbances during flight.

[0060] S50: Control signal output and execution

[0061] The control signals generated by the sliding mode controller are transmitted to the servo drive module. The servo adjusts the quadrotor motor speed based on the control signals, achieving rapid response and stable adjustment of the drone's attitude, ensuring flight safety and accuracy.

[0062] S60: State feedback and adaptive adjustment of control parameters

[0063] The flight control system continuously receives sensor data and updates attitude angle and angular velocity estimates in real time. Based on feedback errors, it automatically adjusts sliding mode control parameters (such as sliding surface parameters and control gains) to adapt to changes in the flight environment, optimize control performance, and improve system stability and responsiveness.

[0064] In step S10: the system completes the initialization of the drone, including obtaining basic state information such as the initial attitude angle and angular velocity, in preparation for subsequent sensor data fusion and control. This step includes the following sub-processes S11 to S12:

[0065] S11: Initial data acquisition and attitude angle calculation of inertial measurement unit

[0066] The drone obtains the initial angular velocity values ​​around the three axes through the built-in gyroscope, and combines it with the static acceleration data measured by the accelerometer to calculate the initial pitch angle θ (Pitch) and roll angle φ (Roll). The attitude angle calculation is usually determined by the angle between the direction of the gravity vector measured by the accelerometer and the reference coordinate system. The specific calculation method is:

[0067]

[0068] Among them, a x ,a y ,a z The yaw angle (Yaw) is determined by measuring the Earth's magnetic field using a magnetometer and using magnetic field projection and trigonometric calculations. This initial attitude angle provides a reference for subsequent state estimation.

[0069] S12: Filter state variable initialization and parameter setting

[0070] The gyroscope bias and accelerometer errors are calibrated to initialize the state vector of the extended Kalman filter, including state variables such as attitude angle, angular velocity, and sensor bias. The covariance matrix is ​​also initialized to determine the covariance of process noise and measurement noise, ensuring good filter convergence and stability.

[0071] In step S20: multi-sensor real-time acquisition and preprocessing phase, the system synchronously acquires gyroscope, accelerometer, magnetometer and GPS data, and performs filtering and anomaly detection on them to ensure data quality and time sequence consistency. This step specifically includes the following processes S21-S22:

[0072] S21: Synchronous acquisition of multi-sensor data

[0073] The drone synchronizes sensor data such as the gyroscope, accelerometer, magnetometer, and GPS through the system clock, ensuring consistent timestamps across different sensor data. This synchronization mechanism can employ hardware triggering or software timestamp correction to prevent data timing distortion from affecting state estimation.

[0074] S22: Data Filtering and Anomaly Detection

[0075] Digital filters (such as low-pass filters) are used to remove high-frequency noise in sensor signals, and statistical methods (such as sliding window anomaly detection) are combined to eliminate obvious abnormal data points to prevent errors from affecting subsequent estimation and control.

[0076] In step S30: Based on the collected multi-source sensor data, the flight control system uses the extended Kalman filter algorithm to dynamically estimate the attitude angle and angular velocity, filter out the effects of noise and drift, and output a high-precision state estimate. This step includes the following processes S31-S32:

[0077] S31: Extended Kalman Filter State Fusion

[0078] Based on the UAV's motion dynamics model, the extended Kalman filter is used to fuse the gyroscope's angular velocity and the acceleration data measured by the accelerometer to estimate the UAV's attitude angle and angular velocity. The process includes a prediction step and an update step:

[0079] Prediction step: Predict the current state based on the previous state and motion model, and predict the covariance matrix.

[0080] Update step: Use the current sensor observations to calculate the Kalman gain, update the state estimate and covariance matrix, and correct the prediction error.

[0081] The filter effectively eliminates gyroscope drift and accelerometer noise, providing high-precision state estimation.

[0082] S32: Dynamic status update

[0083] The filter outputs the current attitude angle error and angular velocity error, updates the state vector, and ensures that the control system obtains accurate real-time state information.

[0084] In step S40: the system designs a sliding mode controller to generate control input based on the estimated attitude error and angular velocity error, achieving fast and robust adjustment of the UAV attitude. The detailed process of this step includes S41 to S43:

[0085] S41: Sliding surface definition

[0086] The sliding surface is defined based on the attitude angle error:

[0087]

[0088] Among them, e i (t) = θ i (t)-θ i,d (t) is the error of the i-th attitude angle, λ i >0 is the adjustment parameter, which determines the error convergence speed and system stability, θ i (t) is the current actual attitude angle, θ i,d (t) is the expected actual attitude angle.

[0089] S42: Sliding Mode Control Law Design

[0090] The control law of the sliding mode controller is designed as:

[0091] Where: u eq,i (t) is the equivalent control used to compensate for the known dynamics of the system; k i >0 is the control gain, which ensures that the system reaches the sliding surface; sat(·) is the saturation function, which limits the control signal to prevent high-frequency chattering; φ i is the boundary layer width, which adjusts the smoothness of the sliding mode control.

[0092] This control law ensures that the system state converges quickly to the sliding surface, achieves highly robust control, and suppresses model uncertainties and external disturbances.

[0093] S43: Control signal calculation

[0094] According to the attitude angle real-time state error e i (t) Calculate the control input for each axis to form the final sliding mode control signal. The control signal includes the speed adjustment for each motor to ensure stable control of the drone's attitude.

[0095] In step S50: After the control signal is output by the sliding mode controller, it is transmitted to the servo drive module to drive the quadcopter motor to adjust the thrust and complete the real-time control of the drone's attitude. This step includes processes S51 to S52:

[0096] S51: Control signal transmission

[0097] The flight control system sends the sliding mode control output signal to the electronic speed controllers (ESCs) of the four motors through the communication bus.

[0098] S52: Motor execution control

[0099] The electronic speed controller adjusts the motor speed according to the control signal, changes the thrust, and controls the attitude angle and flight trajectory of the drone.

[0100] In step S60: the flight control system continuously receives status feedback information from the sensor and dynamically adjusts the sliding mode control parameters according to the feedback error to ensure that the system adapts to changes in the flight environment and maintains stability and control accuracy. This step specifically includes S61 to S62:

[0101] S61: Real-time status feedback

[0102] The flight control system continuously receives the latest data from the gyroscope, accelerometer and magnetometer, obtains the latest estimation of attitude angle and angular velocity through filter fusion, and feeds it back to the flight control calculation unit.

[0103] S62: Online adaptation of sliding mode control parameters

[0104] By monitoring the system error and dynamic response, the sliding surface parameter λ is adaptively adjusted. i , control gain k i and the boundary layer width φ i , which enables the sliding mode controller to adjust the control strategy in real time according to changes in the flight environment (such as wind speed and load changes), ensuring control stability and response speed and preventing excessive vibration or control hysteresis.

[0105] In summary, the quadcopter UAV flight control system based on sliding mode control proposed in this invention, combined with high-precision state estimation of multi-sensor fusion and robust sliding mode controller design, significantly improves the robustness, response speed and ability to adapt to complex environments of the flight control system, ensuring the stability and safety of the UAV flight process.

[0106] It should be noted that the above embodiments are only used to better understand the technical solutions and core concepts of the present invention, and are not intended to limit the scope of protection of the present invention. For those skilled in the art, various forms of equivalent replacements, functional transformations, or structural adjustments can be made to the above embodiments without departing from the spirit and technical concept of the present invention, and these should all be considered to be within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope defined by the claims, and should not be limited to the specific embodiments disclosed in the specification and drawings.

[0107] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any way. Any simple modification, change and equivalent structural change made to the above embodiment based on the technical essence of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A precise control method for a steering gear based on an optimization control algorithm, characterized in that: include: Step 1: Use multiple sensors to collect key operating parameters of the servo in real time, process the data and form a state vector as the basic input data; Step 2: Use an adaptive filtering algorithm to fuse the data collected by the sensors, adjust the model parameters online based on the input-output state vector data at each moment, filter out high-frequency noise, and dynamically estimate the state information of the aircraft: attitude angle and corresponding angular velocity; Step 3: Construct the sliding surface of the servo system based on the system dynamic error, and combine it with the filtered state information to measure the deviation between the actual state of the servo and the desired target; construct the approach law of the sliding surface to generate the control law model to ensure that the system state quickly and stably approaches the sliding surface and slides along it to reach the desired state; according to the real-time state error of the attitude angle e i (t) Calculate the control input of each axis of the aircraft and generate the final sliding mode control signal; Step 4: Convert the control signal into a voltage or PWM signal and input it into the servo to change the thrust to achieve attitude or position adjustment and control; Step 5: Monitor the current state of the servo in real time and compare it with the desired state to form a closed-loop control structure to achieve dynamic correction and real-time optimization.

2. The steering gear precise control method based on the optimization control algorithm according to claim 1 is characterized in that: The key operating parameters include angle, speed, and current data; the multiple sensors include but are not limited to an inertial measurement unit, a gyroscope, an accelerometer, a magnetometer, and a GPS sensor.

3. The steering gear precise control method based on the optimization control algorithm according to claim 1 is characterized in that: The data processing of the key operating parameters includes: Use timestamp correction method to synchronize data clock; A digital filter is used to remove high-frequency noise from the sensor signal, and a sliding window anomaly detection method is used to remove obvious abnormal data points.

4. The steering gear precise control method based on the optimization control algorithm according to claim 1, characterized in that: The adaptive filtering algorithm is an extended Kalman filter or a weighted complementary filter method; the model parameters are adjusted online based on the input-output state vector data at each moment, high-frequency noise is filtered out, and the attitude angle and corresponding angular velocity of the aircraft are dynamically estimated, including: Predict the current state based on the previous state and motion model, and predict the covariance matrix; Use the current sensor observations to calculate the Kalman gain, update the state estimate and covariance matrix, and correct the prediction error; Output the current attitude angle error and angular velocity error, and update the state vector.

5. The steering gear precise control method based on the optimization control algorithm according to claim 1, characterized in that: The sliding surface is defined as: Among them, e i (t) = θ i (t)-θ i,d (t) is the error of the i-th attitude angle, λ i >0 is the adjustment parameter, which determines the error convergence speed and system stability; The sliding mode control law is: Among them, u eq,i (t) is the equivalent control used to compensate for the known dynamics of the system; k i >0 is the control gain, which ensures that the system reaches the sliding surface; sat(·) is the saturation function, which limits the control signal to prevent high-frequency chattering; φ i is the boundary layer width, which adjusts the smoothness of the sliding mode control; According to the attitude angle real-time state error e i (t) Calculate the control input of each axis and generate the final sliding mode control signal: including the speed adjustment amount for each motor.

6. The steering gear precise control method based on the optimization control algorithm according to claim 1 is characterized in that: The real-time monitoring of the current state of the servo and comparison of it with the desired state form a closed-loop control structure to achieve dynamic correction and real-time optimization, including: Continuously receive the latest data from the gyroscope, accelerometer, and magnetometer, and fuse them through an adaptive filtering algorithm to obtain the latest state estimates of attitude angle and angular velocity; By monitoring the system error and dynamic response, the sliding surface parameter λ is adaptively adjusted. i , control gain k i and the boundary layer width φ i , which enables the sliding mode controller to adjust the sliding mode control signal in real time according to changes in the flight environment, ensuring control stability and response speed, and preventing excessive vibration or control hysteresis.

7. The steering gear precision control system based on the optimization control algorithm is characterized by: include: The data acquisition module receives key operating parameters of the servo collected in real time by various sensors, processes the data to form a state vector, which serves as the basic input data of the control system; The adaptive filtering module is used to perform adaptive filtering and fusion processing on the data collected by the sensors, adjust the model parameters online according to the input-output data at each moment, filter out high-frequency noise, and dynamically estimate the state information of the aircraft: attitude angle and corresponding angular velocity; The sliding mode control module constructs the sliding mode surface of the servo system based on the system dynamic error, and combines the filtered state information to measure the deviation between the actual state of the servo and the desired target; constructs the approach law of the sliding mode surface to generate the control law to ensure that the system state quickly and stably approaches the sliding mode surface and slides along it to reach the desired state; according to the real-time state error of the attitude angle e i (t) Calculate the control input of each axis of the aircraft and generate the final sliding mode control signal; The drive execution module is used to convert the control signal into a voltage or PWM signal and input it into the servo, changing the thrust to achieve attitude or position adjustment and control; The real-time feedback module monitors the current state of the servo in real time and compares it with the desired state to form a closed-loop control structure, achieving dynamic correction and real-time optimization.

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