1045 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 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 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 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 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 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 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 relates to the technical field of mobile robot control, and discloses a wheeled robot trajectory tracking anti-interference control system, which comprises a nonlinear dynamics modeling module used for establishing a nonlinear dynamics model of a wheeled robot so as to describe the motion characteristics of the robot under the influence of ground friction force, sliding force and modeling errors; the disturbance estimation module is used for estimating external disturbance and system modeling errors in real time through an extended state observer and providing disturbance compensation data for the controller; and the sliding mode control module is used for realizing rapid convergence of track deviation by constructing a sliding mode surface and enhancing the anti-interference capability of the system by dynamically adjusting the sliding mode gain. According to the invention, through nonlinear dynamic modeling, disturbance estimation, sliding mode control and model prediction control fusion and real-time trajectory optimization, high-precision trajectory tracking is realized, and the anti-interference capability and the dynamic environment adaptability are enhanced.

The invention relates to the technical field of underwater desilting robot cruise control, in particular to an underwater desilting robot intelligent cruise control method based on environmental perception, and the method comprises the steps: determining a track deviation distance by measuring the position distance of an underwater desilting robot between each moment and all moments within a preset duration before; and determining a second gain adjustment coefficient based on the variation trend of the track deviation distance at all moments within a preset time period before the current moment, and determining a gain adjustment coefficient in combination with the first gain adjustment coefficient so as to optimize the gain coefficient of the sliding mode controller and control the attitude of the underwater dredging robot. According to the method, the problem of hysteresis course errors of the underwater dredging robot caused by fixed gain in traditional sliding mode control is solved, gain adaptive optimization is realized by dynamically fusing multi-source trend data, and the dredging efficiency of the underwater dredging robot is improved.

The invention discloses an EHA drive control system three-closed-loop control system and a dynamic hysteresis compensation method. The technical problems that an existing electro-hydrostatic actuator is large in hydraulic hysteresis error, poor in dynamic response and insufficient in environmental adaptability are solved. The method comprises a three-stage control structure of a position control loop, a speed control loop and a current control loop, the position control loop introduces load disturbance feed-forward compensation based on a state observer, the speed control loop adopts an adaptive sliding mode control strategy, the current control loop executes a decoupling vector control algorithm, and the load disturbance feed-forward compensation is realized. And a dynamic hysteresis compensation model based on historical input and disturbance estimation is established in the controller. According to the system, the servo motor, the gear pump, the hydraulic cylinder and the sensor module are integrated into an integrated drive control structure, and the effects of improving the control precision, reducing the response time delay and correcting the hydraulic hysteresis error are achieved.

The invention discloses an open-phase self-fault-tolerant control method for a phase-shift-free dual three-phase permanent magnet synchronous motor. The method can automatically adjust a current distribution relation in the case of an open-phase fault and suppress torque pulsation in combination with neutral point voltage compensation. Comprising the following steps: driving two pairs of three-phase winding modules of the phase-shift-free DTP-PMSM in parallel by adopting two two-level three-phase voltage source inverters, and providing a mathematical model of the motor; current distribution is automatically adjusted; compensating the voltage of the neutral point, and calculating the offset of the fault module; the current is redistributed to suppress the torque ripple after the fault, and the secondary oblique wave component generated by the phase failure fault is eliminated; a current loop controller is improved through dead-beat predictive control; the speed ring controller is optimally designed into an improved sliding mode controller; an improved double-loop controller system is formed by combining improved sliding mode control and dead-beat prediction control, high-gain parameters are set to improve the dynamic response performance of the system, and a new self-fault-tolerant control system is formed.

The invention provides a fault-tolerant control method for preset performance of a morphing aircraft based on sliding-mode control. The fault-tolerant control method comprises the following steps: establishing a longitudinal nonlinear dynamic model of the morphing aircraft; based on the longitudinal nonlinear dynamic model of the morphing aircraft, establishing a longitudinal nonlinear dynamic model of the morphing aircraft under the fault condition of the execution mechanism; the method comprises the following steps: extracting a speed subsystem and an attitude subsystem aiming at a longitudinal nonlinear dynamic model of a morphing aircraft under an execution mechanism fault condition; carrying out sliding-mode control-based preset performance fault-tolerant controller design on the attitude subsystem; aiming at a speed subsystem, combining an attitude subsystem controller, and carrying out sliding mode control-based preset performance fault-tolerant controller design; the two designed controllers are utilized to realize the fault-tolerant control of the variant aircraft under the condition of executing mechanism fault. The sliding mode control and the preset performance control are combined, so that the conservative property is improved by quick response, the fault-tolerant capability is enhanced by switching the control law, and the stability is improved.

The invention discloses a grid-connected inverter complementary sliding mode control method based on a cascade generalized proportional-integral observer, and the method comprises the steps: firstly building a mathematical model of a grid-connected inverter, and enabling the mathematical model to be used for the design of a controller; and a cascade generalized proportional integral observer is introduced to estimate the lumped disturbance of the hyper-local model so as to improve the anti-disturbance capability and the noise suppression capability of the system. By introducing the complementary sliding mode controller, compared with a traditional sliding mode, the position tracking error is reduced by half. By introducing the improved reaching law, a large reaching speed can be kept when the system state is far away from the sliding mode surface, and the sliding mode reaching speed is weakened when the system state approaches the sliding mode surface, so that the chattering phenomenon of the sliding mode is reduced. According to the control method, the coupling influence of the d axis and the q axis can be obviously inhibited, the harmonic content of the three-phase current is reduced, and stable operation of the grid-connected inverter is facilitated.

The invention belongs to the technical field of active suspension control, and particularly relates to an active suspension control method and system based on Skhook-ADRC-FFORC. According to the method, key parameters are obtained by establishing a 1 / 4 vehicle suspension dynamical model, a state space model is constructed, active disturbance suppression control is introduced on the basis, and the stability of the vehicle suspension is improved. Real-time estimation and compensation of vehicle body displacement and total disturbance of a system are realized, fractional order sliding mode control is combined, a fractional order sliding mode surface is constructed by using a Caputo operator, high robustness and smoothness are considered, the buffeting problem of a traditional sliding mode is suppressed, and finally, the high-frequency vibration isolation advantage of Skyhook control and the anti-interference capability of ADRC-FFORC are subjected to weighted fusion. The problems that in the prior art, model simplification and control distortion are caused, and a single strategy cannot give consideration to multi-target performance are solved, and by fusing ADRC, FOSMC and Skyhook control and realizing adaptive weighted switching, the robustness, smoothness and real-time performance of the active suspension under complex working conditions are effectively improved, so that collaborative optimization of vehicle comfort and safety is realized.

The invention relates to a clamping control method of a movable mechanical arm. The clamping control method is used for solving the problem of collaborative optimization of stability and precision in clamping operation. According to the method, a motion coupling factor and a contact force gradient mode length are calculated in real time through a dynamic coordination module, base-world coordinate system conversion is combined to realize bidirectional constraint of chassis speed and mechanical arm torque, instability caused by sudden load change is inhibited, a time-varying sliding mode surface and an anti-buffeting control law are adopted for sliding mode control, and the stability of a mechanical arm is improved. Dynamic boundary layer thickness and adaptive gain are introduced, control parameters are further optimized through a double-delay depth deterministic strategy gradient, and a multi-target reward function is designed. According to the method, the robustness of the system is remarkably improved through closed-loop optimization, and the stability and precision of the movable mechanical arm in the clamping operation are improved.

The invention discloses a novel model-free super-spiral sliding mode control method based on a sliding mode disturbance observer, and the method comprises the steps: designing a novel model-free super-spiral sliding mode controller of a rotating speed ring based on the combination of a novel super-local model and a second-order super-spiral law; meanwhile, a sliding mode disturbance observer is designed to estimate uncertain and unknown disturbance parts of system parameters in real time and feed back to a novel model-free super-spiral sliding mode controller of a rotating speed ring, so that the rotating speed of the motor is controlled; the controller introduces a linear system state item and a nonlinear disturbance item according to the novel super-local model, establishes a novel super-local model of the system, combines the novel super-local model of the system with a rotating speed ring state equation of the motor, establishes a novel super-local model of a rotating speed ring, and performs the super-local model of the rotating speed ring; and establishing a novel model-free super-spiral sliding mode control law of the rotating speed ring based on the novel super-local model of the rotating speed ring and an observation result of the sliding mode disturbance observer. The influence of external disturbance on the motor is effectively solved, buffeting is weakened, and effective tracking of the given rotating speed can be achieved.

The invention discloses a commercial vehicle driving control method considering a load non-uniform distribution characteristic. The method comprises the following steps: acquiring and preprocessing sensor data of a commercial vehicle; estimating yaw disturbance torque caused by uneven load distribution in real time; a robust model prediction controller based on yaw disturbance torque compensation is constructed, and multi-axis coordinated yaw torque meeting the driving stability requirement under the non-uniform load working condition is output; driving force optimization distribution is carried out, and target driving force of each electric wheel is determined; and designing a driving force synchronous tracking controller based on the tire relaxation characteristic model, and outputting the torque of each electric wheel to complete the driving stability control of the commercial vehicle. According to the method, the yaw disturbance torque observer and robust model predictive control are fused, and the disturbance yaw torque is estimated in real time and compensated in a feedforward mode, so that pertinence and fineness are better in the aspect of disturbance suppression; the auxiliary sliding mode control can further improve the robustness of the system in an unknown or rapidly changing external environment.

The invention relates to the technical field of automatic driving, in particular to an automatic driving system based on intelligent traffic, which comprises a multi-source heterogeneous sensing fusion unit, a group collaborative decision-making unit and a dynamic control optimization unit, the group collaborative decision-making unit carries out space-time alignment on multi-source heterogeneous data, constructs and updates a global environment perception knowledge graph, carries out modeling on vehicle trajectories based on a Monte Carlo tree search framework in combination with deep reinforcement learning and risk perception, and trains a deep Q network model by adopting federated learning and adversarial training. A traffic flow distribution scheme is generated by applying multi-agent deep reinforcement learning and a game theory, storage data is proved by using homomorphic encryption and zero knowledge, a dynamic control optimization unit predicts, controls and plans an acceleration curve through a nonlinear model, and a transverse path tracking error is controlled by fusing sliding mode control and a fuzzy logic algorithm. And the safety of automatic driving is improved.

The invention provides a redundant pneumatic parallel platform active disturbance rejection control method based on driving force decoupling optimization, and the method comprises the following steps: firstly, carrying out the real-time quantification and solving a state variable and unpredictable disturbance of a pneumatic redundant parallel platform under the optimal driving force; the control accuracy and the response speed are improved through total disturbance feedforward compensation, and the influence of external interference on the system performance is effectively restrained; secondly, designing a proper sliding mode control law based on the estimated internal state of the system and the total disturbance, so as to ensure that the state of the system stably and quickly approaches a sliding mode surface; according to the invention, through estimating the total disturbance suffered by the redundant pneumatic parallel platform in the motion process and carrying out feedforward compensation, a self-adaptive gain sliding mode control law is further constructed so as to ensure that the error change between the system state and the sliding mode surface can dynamically adjust the control gain; and the control precision and stability of the complex nonlinear redundant pneumatic parallel system under the conditions of internal state uncertainty and external interference are effectively improved.