1291 results about "Sliding mode control" patented technology

The invention relates to the technical field of devices for adjusting, controlling or stabilizing power or frequency in a power grid, in particular to a rapid frequency modulation method for a wind turbine generator based on disturbance adaptive compensation, which comprises the following steps of: constructing a dynamic disturbance sensing data set by collecting frequency deviation, a frequency deviation change rate and fan state parameters; a window length and a weight coefficient of a short-time window sliding mean algorithm are dynamically optimized by adopting a genetic algorithm, background noise interference of a power grid is suppressed, and high-precision disturbance characteristics are extracted. And reducing frequency interference of inertia identification by using a reverse test signal. And updating boundary layer parameters of the sliding mode control model through an online incremental learning algorithm, dynamically adjusting the output priority based on the disturbance energy entropy, and eliminating power conflicts. A multi-island genetic algorithm is introduced to optimize a mode switching threshold value, and frequency modulation safe exit is realized in combination with adaptive power ramp rate limitation. According to the method, the frequency response speed and the multi-source cooperation efficiency in the dynamic multi-disturbance and inertia time-varying scene are remarkably improved, and the frequency secondary drop risk is reduced.

The invention discloses a grinding and polishing control method and system based on sliding mode control, and relates to the field of robot industrial control. The method comprises the steps that a force signal of a force sensor at the tail end of a mechanical arm is collected and decomposed into a normal force component perpendicular to the surface of a workpiece; determining a deviation value between the normal force component and preset target pressure, and constructing a sliding mode surface expression of sliding mode control; determining a force control component according to the force control gain coefficient; position deviation and speed deviation are determined according to the position signals of the joint encoders and a preset polishing track; determining a position control component according to the position deviation, the speed deviation, the position gain coefficient and the speed gain coefficient; constructing a selection matrix, and performing orthogonal decomposition and superposition on the force control component and the position control component to obtain a comprehensive control instruction; and the driving signals are converted into driving signals of the joint motors, and the driving signals are transmitted to the mechanical arm for grinding and polishing. By implementing the method, force control of robot grinding and polishing can be optimized, and response delay is reduced.

The present application relates to the technical field of electric vehicles, and discloses a fault-tolerant actuator control method and apparatus for an electric vehicle, a storage medium, and a vehicle. The method comprises: acquiring vehicle-mounted sensor information and a reference trajectory of a distributed drive electric vehicle; using an MPC algorithm to perform trajectory tracking on the basis of the vehicle-mounted sensor information and the reference trajectory to obtain an expected front wheel steering angle; when at least one steering actuator is determined to be faulty on the basis of the vehicle-mounted sensor information, calculating a differential power moment on the basis of the expected front wheel steering angle and an actual front wheel steering angle corresponding to a previous moment by using a sliding mode control (SMC) algorithm; when at least one driving actuator is determined to be faulty on the basis of the vehicle-mounted sensor information, calculating a torque corresponding to the current moment on the basis of the differential power moment by using a distribution optimization algorithm based on minimum tire adhesion utilization; and controlling steering of the distributed drive electric vehicle on the basis of the torque corresponding to the current moment. The present application implements reliable control of the vehicle in a case where a steering actuator and a driving actuator fail at the same time.

The invention relates to the technical field of vehicle cruise control, and discloses a neural network driven vehicle adaptive cruise control method. Real-time driving data are collected through a multi-modal sensor, spatial-temporal feature fusion is carried out through a multi-layer convolutional neural network to generate dynamic environment sensing data, and a pre-trained adaptive decision model is input to obtain driving strategy parameters. And constructing a mixed integer programming model on the basis, globally planning a cruise path by adopting an incremental branch and bound algorithm integrating a dynamic relaxation threshold and a heuristic pruning strategy, and outputting optimal cruise trajectory data. A hierarchical control framework comprising a planning layer, a coordination layer and an execution layer is constructed, the planning layer generates a global trajectory sequence, the coordination layer dynamically corrects a local trajectory, and the execution layer achieves vehicle longitudinal acceleration and transverse steering angle tracking based on a robust sliding mode control algorithm and outputs a vehicle control instruction to complete self-adaptive cruise control. And the cruise control performance, safety and stability of the vehicle in a complex environment are improved.

The invention discloses an unmanned aerial vehicle-mechanical arm system cooperative control method for precise spraying. According to the method, through a depth camera and a point cloud reconstruction algorithm, three-dimensional modeling of a target surface and semantic recognition of a spraying area are achieved, and a spraying path and spraying posture data are generated. Based on spraying task requirements, a spraying film thickness experience estimation model is established, and path point-level spraying parameters are obtained. Load mass change, mechanical arm mass center change and spraying reaction force of each path point are calculated, and a coupling dynamic model is constructed in combination with the system state. And dynamic compensation control input is generated by using an adaptive sliding mode control algorithm. After spraying is completed, the actual spraying effect is collected through image and point cloud detection, color, film thickness and texture features are extracted and compared with task requirements, error feedback is generated, and control parameters are adjusted online. According to the method, the problems of dynamic change and error compensation in the spraying process are effectively solved, and the method is suitable for a high-precision spraying task in a complex environment.

The invention discloses a virtual marshalling multi-train cooperative adaptive sliding mode control method based on an extended state observer, and the method comprises the steps: firstly constructing an urban rail multi-train tracking error dynamic model based on a unit train dynamic equation; secondly, establishing a nonsingular terminal sliding mode surface according to the urban rail multi-train tracking error dynamical model to obtain an equivalent control law, combining the equivalent control law with a switching control law to obtain a nonsingular terminal sliding mode control law, and introducing a gain adaptive law and a saturation function into the nonsingular terminal sliding mode control law to obtain an adaptive nonsingular terminal sliding mode control law; and finally, an extended state observer is adopted to estimate external interference, an observation value of the interference is added to the output of the adaptive nonsingular terminal sliding mode controller to serve as feed-forward compensation, and the adaptive nonsingular terminal sliding mode control method based on the extended state observer is constructed. According to the method, the multi-train cooperative operation tracking control precision and stability can be remarkably improved under the external interference condition.

The invention relates to the technical field of robot intelligent control, in particular to a concrete construction digital intelligent robot control system. Comprising a concrete data acquisition module, a construction path planning module, a robot control module, a quality monitoring module and an interactive feedback module. The data acquisition module acquires information such as concrete height and humidity of a construction area through a sensor; the path planning module generates an optimal construction path by using an artificial bee colony algorithm; the robot control module accurately executes a path instruction through sliding mode control and a fuzzy adaptive algorithm; the quality monitoring module adopts a support vector machine to evaluate construction quality and generate a report; and the interaction feedback module realizes path adjustment and information interaction through a visual page. Through combination of digital intelligence and algorithm optimization, integration of intelligent planning of a construction path, precise control of the robot and quality monitoring and evaluation is realized, and the intelligent level and engineering quality of concrete construction are remarkably improved.

The invention relates to a high sea condition unmanned ship dynamic anti-interference control method based on intelligent body.The method comprises the following steps that information is collected, multi-source data are integrated through a data fusion algorithm, and multi-mode sensing data are obtained; constructing a body-equipped intelligent model, taking the multi-modal sensing data as input and the unmanned ship control instruction as output, and performing offline training; constructing an interference prediction model to predict the change trend of interference factors under the high sea condition, and performing adaptive interference compensation according to an interference prediction result; a hierarchical control structure is designed, a task planning layer makes a global navigation plan based on risk assessment, a motion control layer adopts a model prediction control and adaptive sliding mode control combined algorithm to convert instructions, and an execution mechanism layer drives an execution mechanism; meanwhile, online self-adaptive optimization is carried out, and the intelligent model with the body is updated and optimized according to feedback information. Compared with the prior art, the dynamic anti-interference capability, the operation precision and the reliability of the unmanned surface vehicle under the high sea condition are remarkably improved, the environment adaptability and the autonomous operation level of the unmanned surface vehicle are enhanced, and the unmanned surface vehicle is suitable for various high sea condition ocean operation scenes.

The invention provides a mechanical arm control system and control method of a photovoltaic panel intelligent installation robot, and relates to the technical field of photovoltaic equipment installation. According to the method, a three-dimensional point cloud map is constructed, the curvature characteristics of the surface of an obstacle are recognized, meanwhile, a joint angle is fed back through a magnetic encoder, and normal pressure and tangential friction force are monitored through a sensor; when the normal pressure exceeds a threshold value, a reverse displacement compensation amount is calculated based on reverse dynamics, a compensation instruction is generated, and a safe approaching direction is determined in combination with curvature characteristics; and the joint angular velocity and the torque threshold are adjusted in real time through a nonlinear sliding mode control algorithm, a smooth joint target torque curve is generated, and it is ensured that the mechanical arm stably tracks the path and the tail end friction force is controlled. And in an abnormal condition, the system triggers an alarm and freezes the mechanical arm until the operator confirms the compensation strategy. The control precision and adaptability of the mechanical arm are improved, the photovoltaic panel is effectively prevented from being damaged, and the installation efficiency and reliability are improved.

The invention discloses a fixed-wing unmanned aerial vehicle trajectory tracking control system and method based on a disturbance observer. The system acquires various state data of the unmanned aerial vehicle through the data acquisition module, the interference observer module estimates external interference in real time based on a dynamic model, the trajectory planning module generates an optimized trajectory according to tasks and environments, and the controller module realizes accurate control by adopting a model prediction control and sliding mode control composite strategy. And the execution mechanism driving module executes the control instruction. The method comprises the steps of data acquisition, interference estimation, trajectory planning, controller design and calculation, actuating mechanism driving and the like. The method can effectively improve the trajectory tracking accuracy and stability of the fixed-wing unmanned aerial vehicle in a complex environment, and has a wide application prospect.

The invention relates to the technical field of aerocar mode switching, and discloses a distributed driving aerocar self-adaptive fault-tolerant control method for actuator multimode failure, which comprises the following steps: constructing a unified six-degree-of-freedom dual-mode state space model; residual signals are generated based on extended Kalman filtering and a sliding-mode observer, and fault types and positions are positioned in real time through a lightweight classifier; the method comprises the following steps: extracting residual time-frequency features, identifying hard faults by using a lightweight convolutional neural network, quantifying soft fault degrees through an incremental support vector machine, fusing multi-source information based on a Bayesian network to output fault types, levels and confidence coefficients, and introducing an incremental learning mechanism to realize self-evolution of a diagnosis model; a virtual control instruction is generated by adopting hierarchical sliding mode control, thrust and torque distribution of remaining actuators is optimized based on a dynamic quadratic programming algorithm, control parameters are adjusted online in combination with a Lyapunov adaptive law, aerodynamic interference and model uncertainty are inhibited, attitude stability and trajectory tracking in air-ground mode switching are guaranteed, and the method has the advantages of being high in reliability and high in reliability. And the fault-tolerant performance and the operation safety of the hovercar in the air-ground mode switching process are obviously enhanced.

The invention discloses an anti-carsickness semi-active seat band-stop filtering control method, which comprises the following steps of: firstly, establishing a two-degree-of-freedom model of a vehicle seat suspension system, and designing a seat suspension active control system to meet the following constraint conditions: the dynamic stroke of a seat suspension does not exceed the maximum allowable stroke; and the control force is smaller than the maximum output control force of the actuator. In order to effectively reduce the carsickness symptom caused by seat vibration, the invention also establishes a target function for inhibiting the vibration of the seat in a human body sensitive frequency range, designs a frequency weighted sliding mode control algorithm, accurately adjusts the damping coefficient of the magnetorheological damper through a control law, inhibits the low-frequency vibration in the frequency range of 0.1-0.5 Hz, and improves the safety of the seat. The riding comfort is improved, and the motion sickness is reduced.

The invention relates to the technical field of backlight brightness control, in particular to a Mini LED backlight brightness control system applied to display equipment. The future illumination change trend is predicted through MPC, and a control sequence of backlight adjustment is optimized, so that the control sequence responds to the change in a short time, and meanwhile, the severe fluctuation of brightness is reduced; the sliding mode control is introduced for robust feedback control aiming at ambient light disturbance and sensor errors, so that the system can still maintain stable brightness output under external disturbance, and the LED backlight system not only can improve the visual comfort of a user, but also realizes optimization of energy consumption.

The invention discloses a path planning and cooperative control method for a heterogeneous unmanned cluster system in a dynamic environment. The method comprises the following steps: firstly, constructing a distributed position observer of predefined time, and estimating initial and final formation target positions of each follower unmanned aerial vehicle by utilizing local information interaction between the unmanned aerial vehicles; constructing a global path planner based on an adaptive variable solution space RRT algorithm, and planning a safe global path when each unmanned aerial vehicle passes through the dense obstacle area; a local path planner based on a predefined time prediction filter is constructed, and the potential collision risk between the unmanned aerial vehicle and a dynamic obstacle in formation tracking is avoided; and finally, performing motion control on the unmanned aerial vehicle by adopting a sliding mode control method according to the safe global path and the local path planning result, thereby realizing fixed-time formation tracking control of the unmanned aerial vehicle cluster. By using the method, the multi-unmanned aerial vehicle system can realize safe flight in a complex dynamic environment, and the control performance when a formation tracking flight task is realized is ensured.

The invention relates to an unmanned aerial vehicle self-adaptive wind-resistant flight control method and system based on multi-mode environment perception. The method comprises the steps that a miniature wind speed and direction sensor array obtains real-time wind speed and wind direction information of the surrounding environment of an unmanned aerial vehicle; the inertial measurement unit obtains real-time attitude angle, angular velocity and acceleration information of the unmanned aerial vehicle; the visual flow sensor obtains real-time image data of the surrounding environment of the unmanned aerial vehicle; the data fusion and wind field estimation module receives multi-source data collected by the miniature wind speed and direction sensor array, the inertial measurement unit and the visual flow sensor, carries out fusion processing on the multi-source data based on an extended Kalman filtering algorithm, and predicts to obtain predicted wind field information in a future short time window; the flight control core module adopts an adaptive sliding mode control algorithm to calculate a compensation control quantity for counteracting wind disturbance, and performs real-time control on the unmanned aerial vehicle based on the compensation control quantity; and the wind resistance and the operation reliability of the unmanned aerial vehicle in a complex dynamic environment are improved.

The invention relates to an aircraft high-speed throwing control method and device and electronic equipment, and relates to the technical field of aircraft flight control, and the method comprises the steps: calculating an optimal control instruction sequence in a preset time domain in a rolling manner based on an expected throwing trajectory, a predicted throwing trajectory model and a multi-objective optimization function, and generating a main control quantity, the main control quantity is a control instruction in the optimal control instruction sequence; performing feedforward compensation on the main control quantity based on the disturbance observation result, and performing superposition processing of the sliding mode control quantity on the main control quantity after feedforward compensation based on the disturbance observation result to obtain a superposed main control quantity; high speed throwing of the aircraft cargo is performed based on the superimposed master control quantity. Through application of the scheme, the problem of throwing track deviation caused by posture change and airflow disturbance is effectively solved, it is ensured that the goods can accurately reach the target position according to expectation, and the accuracy of goods throwing is remarkably improved.

The invention relates to the field of high-altitude operation equipment and automatic control, and discloses a control system and method for a self-walking insulating lifting platform, and the system comprises a posture sensing module which is used for collecting the inclination angle and angular velocity information of the platform in real time; the dynamic modeling module is used for establishing a multi-degree-of-freedom dynamic model of the platform; the optimal control module is used for calculating optimal control input through objective function optimization; and the robust control module is used for realizing anti-interference leveling of the platform under an external disturbance condition based on a sliding mode control algorithm and in combination with adaptive gain adjustment. The method comprises the steps of obtaining real-time attitude information of a platform, establishing a multi-degree-of-freedom dynamic model, defining a weighted objective function and solving optimal control input, calculating control input based on a sliding mode control algorithm, and adjusting the positions of platform supporting legs to achieve a stable leveling state. According to the invention, the leveling precision and the anti-interference capability of the platform are improved, and the method is suitable for safe and stable operation under various complex terrain conditions.

The invention discloses a target tracking method fusing visual perception and motion control. The method comprises the following steps: improving a YOLOv8 model and pre-training the improved YOLOv8 model; s2, preprocessing an image acquired by a camera, inputting the preprocessed image into the YOLOv8 model trained in the step S1, and outputting a target bounding box and a target center pixel coordinate; target center pixel coordinates are transmitted to a development board of a pre-burning algorithm program through a serial port, the development board is designed according to a stepping motor mathematical model, sine acceleration and deceleration serve as the basis, PID control, sliding mode control and fuzzy control algorithms are fused, and code burning is generated after in-loop simulation optimization; and the development board drives a stepping motor to adjust the posture of the holder according to the target center pixel coordinate control instruction, so that the target stops moving after being kept in the view center. The problems that a traditional YOLOv8 model is large in target recognition error under the complex background, PID control is prone to oscillation under the nonlinear working condition, buffeting exists in sliding mode control, and response lags when fuzzy control is independently used are solved.

The invention discloses a millisecond regulation and control method for hydrogen fluoride production based on reinforcement learning and model prediction control, and relates to the field of hydrogen fluoride production regulation and control. In the multi-scale neural symbol dynamics modeling step, GNN, a cellular automaton and a state space model are fused, and parameters are updated in real time; in the causal reinforcement learning decision optimization step, an effect is calculated through a causal graph, and a reward function is optimized; in the time-space fractional order sliding mode control step, a fractional order sliding mode surface and a controller are designed, and rapid and stable control is achieved; in the memory enhancement element learning adaptation step, a DNC storage strategy is utilized, new working conditions are quickly adapted through element gradient, and millisecond-level precise regulation and control are achieved. According to the method, prediction errors are greatly reduced, the response speed is increased, and overshoot is reduced; the product yield is improved, the energy consumption is reduced, and new working conditions are quickly adapted; fault detection and risk early warning are more accurate, equipment operation is more stable, production efficiency is effectively improved, cost is reduced, and safety is enhanced.

The invention discloses a robot double-closed-loop sliding mode control method based on NIFPRRL, and the method comprises the steps: firstly considering the performance of a robot under the influence of sliding and sideslip, designing a linear expansion state observer LESO for real-time observation, and improving the anti-interference capability of the robot; secondly, an NIFPRRL double-closed-loop sliding mode controller is designed based on a variable exponential power term, accurate control of an inner loop over the attitude and stable tracking of an outer loop over the position are achieved, and the problem that the trajectory tracking precision is low due to the fact that the robot is prone to disturbance in a complex environment is solved. The control convergence speed and trajectory tracking precision of a robot operation control system can be improved, and it is ensured that the robot has excellent motion control performance under the dynamic condition of complex working conditions.

The invention relates to a four-rotor unmanned aerial vehicle anti-interference trajectory tracking control method based on an observer and a tracking differentiator, and belongs to the field of aerial robot control. According to the method, various aerodynamic disturbances in the environment are estimated in real time by using the nonlinear disturbance observer; then, external interference estimated in real time is used as an interference compensation item of a sliding mode controller to obtain control laws of a four-rotor position subsystem and a four-rotor attitude subsystem, and meanwhile, an improved tracking differentiator is used for providing a smoother expected attitude signal; and finally, proving accurate estimation of the nonlinear disturbance observer on external disturbance through a global consistent final bounded stability theory, and proving the stability of the four-rotor control system through a Lyapunov method. A novel high-frequency switching function established in a control law is combined with an improved tracking differentiator, so that the buffeting problem of sliding mode control is effectively suppressed, and a nonlinear interference observer improves the anti-interference capability of four rotors; the control input buffeting is reduced while the robustness of the four rotors is improved.

The invention discloses a distributed driving automobile control method. The method comprises the steps that 1, dynamic parameters of an automobile are collected in real time, and the equivalent adhesion coefficient of each wheel is calculated; 2, dividing types and optimizing torque distribution according to pavement adhesion coefficients: respectively adopting priority, uniform and dynamic weight distribution strategies for low, high and asymmetric pavements; 3, establishing a multi-objective optimization model, and solving an optimal torque distribution scheme considering the tire utilization rate and the yaw stability by using a quadratic programming algorithm; 4, compensating a torque error and motor response delay by adopting sliding mode control and a feedforward-feedback mechanism; and 5, the control parameters are dynamically corrected based on errors of the longitudinal acceleration and the yaw velocity, and torque limiting or safety mode switching is triggered. Through multi-strategy collaborative optimization, the stability and driving efficiency of the vehicle under different attachment road surfaces are improved, the tire slipping risk is reduced, and driving safety under extreme working conditions is guaranteed.

The invention discloses a control method and system for improving a transient stability area of a grid-connected inverter. It includes performing park coordinate transformation on a three-phase voltage and a three-phase output current of a connection point of a grid-connected inverter to obtain an actual voltage input value and an actual current input value required for current control; obtaining the grid-connected voltage phase through phase-locked loop processing; generating, by the power control module, the current command value required by the current control module; using the sliding mode current control method, by the current control module, to generate a dq voltage signal, and obtaining PWM modulated voltage control signals through inverse park transformation; using a LCL filter to filter out high-order harmonics to achieve grid-connected control. The present invention effectively improves the transient stability of the grid-connected inverter by using the sliding mode control method in the current controller.

The invention discloses a virtual marshalling train distributed control method and system based on running track optimization, and the method comprises the steps: firstly, considering the resultant force in a train running process, and building a train simple substance point kinetic equation; secondly, in the virtual marshalling state, train operation conditions are divided according to the spatial position in combination with a train driving control strategy; thirdly, by means of the virtual marshalling technology, all train units in marshalling in the train running process are made to be consistent in speed, constraint conditions of safety protection distance and space-time safety protection are set, the train running energy consumption and comfort model is introduced into a multi-target hiking optimization algorithm, and the energy consumption and comfort of the train are optimized in combination with the constraint conditions; optimizing the running track according to the working condition state of the train; and finally, establishing a distributed self-adaptive sliding mode control law of the train according to the optimized running track. By optimizing the running track of the train, an accurate protection strategy is realized, and the control method established based on the optimized track can ensure stable running of the train while improving the railway transportation efficiency.

The invention discloses a three-degree-of-freedom pectoral fin cooperative motion control and gait optimization method, which comprises the following steps of: firstly, establishing a motion gait model of a three-degree-of-freedom rigid pectoral fin, and acquiring hydrodynamic feedback and attitude information of the pectoral fin under different input parameter combinations in real time by utilizing a multi-mode sensing system so as to construct a pectoral fin-environment interaction model; secondly, training a high-level strategy network by adopting a deep reinforcement learning algorithm, and optimizing a control signal cbase (t) by taking propulsive efficiency and attitude stability as reward functions; then, constructing a multi-layer hierarchical neural control network to decode cbase (t), outputting an incremental control signal delta c (t), and introducing a central mode generator network to generate a reference trajectory # imgabs0 # of each degree of freedom; finally, dynamic compensation of external disturbance and modeling errors is achieved based on a sliding mode control method of an extended state observer, delta c (t) is adjusted online through a performance evaluation index function J (t) to correct control parameters, pectoral fin motion is kept in the optimal propulsion and stable state all the time, closed-loop optimization is achieved, and the propulsion efficiency and attitude control capacity of the robotic fish are improved.

The invention belongs to the technical field of hypersonic flight vehicle control, and relates to a rigid-elastic coupling-oriented active and passive integrated control method for a hypersonic flight vehicle. The invention aims to realize stable tracking control of the elastic hypersonic flight vehicle. The method comprises the following steps: constructing a longitudinal dynamic model of the elastic hypersonic aircraft; self-adaptive identification of the elastic vibration frequency is realized through a cascaded self-adaptive filter; an elastic modal filtering estimation method is designed, and high-precision and low-cost elastic modal state quantity is provided for subsequent active feedback controller design; and then rigid-elastic coupling model decomposition is carried out, the control performance is ensured by using active disturbance rejection passive control for a rigid body subsystem, an RBF neural network is introduced for an elastic subsystem, an elastic mode is actively inhibited by using sliding mode control, and stable tracking of a reference instruction is realized. The method is an active and passive integrated control method for the hypersonic flight vehicle oriented to rigid-elastic coupling, and the application prospect is wide.

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.

The invention relates to the field of motor control, and discloses a permanent magnet synchronous motor enhanced active-disturbance-rejection control method based on a superhelix control law, which comprises the following steps of: firstly, establishing a permanent magnet synchronous motor mathematical model by considering load disturbance, current sampling disturbance and flux linkage parameter change disturbance in actual motor operation; according to the mathematical model of the permanent magnet synchronous motor and the mathematical characteristics of the active disturbance rejection controller, constructing an observer equation by using a differential relationship, and solving the problem of observation error accumulation through the differential relationship; a lumped disturbance compensation function is introduced into the observer, the anti-interference capacity of the control system is enhanced, and decoupling of steady-state and dynamic performance control parameters is achieved; and finally, designing a state error feedback control law by adopting a super-spiral sliding mode control law, so that the anti-interference capability of the active-disturbance-rejection controller is further improved, and the inherent buffeting of the system is weakened. A simulation result shows that compared with a traditional PI control method, the control method has better dynamic response performance and anti-interference performance.

The invention discloses an electric power control method based on a diesel generator, commercial power and photovoltaic grid connection. The method comprises the following steps: acquiring operation data of commercial power, a diesel generator and photovoltaic-energy storage through an energy management system, constructing a multi-target dynamic optimization model, and distributing power by taking the lowest comprehensive operation cost, the highest power supply reliability and the minimum carbon emission as targets; the power analysis and regulation unit adopts a double-loop mechanism of outer-loop Kalman filtering prediction compensation and inner-loop model prediction control, synchronizes the voltage, frequency and phase of the side to be connected with the grid and the power grid side, and smoothly switches the power change rate of each energy source in the process through a self-adaptive sliding mode control strategy; and the grid-connected switching unit intelligently pre-judges the grid-connected switching opportunity by using a long short-term memory (LSTM) network and fuzzy logic fusion model. According to the invention, the energy utilization efficiency and the power supply stability are improved, multi-energy intelligent dynamic regulation and control are realized, the comprehensive operation cost of the system is effectively reduced, and reliable power supply under complex working conditions is ensured.

The invention discloses an automobile stability coordination control method based on reinforcement learning, and belongs to the technical field of electric automobile control, the method comprises the following steps: using a linear two-degree-of-freedom model to obtain an expected yaw velocity and an expected side slip angle; additional yawing moment and a steering angle of a driving rear wheel are obtained through sliding mode control; acquiring the state type of the current vehicle; constructing an intelligent agent and a reward function of reinforcement learning, and continuously training the intelligent agent until the optimal cooperative control coefficients of the additional yaw moment and the steering angle of the active rear wheel are output, so as to realize the cooperative control of the electric vehicle; the reward function includes a steady state reward, a yaw velocity error and side slip angle error reward, a critical steady state reward, and an anti-jitter reward. According to the invention, the steering and yawing moment of the driving rear wheel can be coordinated and controlled, so that the stability and safety of the vehicle are ensured.