606 results about "Robust control" patented technology

The invention provides an intelligent regulation and control method for a three-stage constructed wetland recirculating aquaculture system, and belongs to the technical field of machine learning. The method comprises the following steps: firstly, continuously collecting water quality data and operation control data of each control unit, and constructing a multi-source heterogeneous data set under a unified time scale; then, constructing a pollution evolution trend prediction model, and capturing a dynamic evolution trend of water quality along with time and control behavior changes; then, under the guidance of a prediction result, analyzing the similarity of historical states and the sensitivity of regulation and control response, automatically identifying key control parameters which influence the water quality change of the system at present, and reasoning the dynamic adjustable boundary of the key control parameters; and finally, constructing a reinforcement learning strategy network fusing state prediction, a parameter boundary and a control target, realizing multi-target tradeoff among pollutant removal efficiency, a water quality standard-reaching rate and operation energy consumption, and outputting an efficient and steady control strategy through continuous interactive training. According to the invention, efficient, accurate and robust operation of the wetland system can be realized.

The invention relates to a control method and device for wind load resistance of a mooring unmanned aerial vehicle and a storage medium. The method comprises the following steps: collecting and modeling a mooring unmanned aerial vehicle to obtain a system dynamics model; state estimation is carried out to obtain system wind load disturbance data and expected attitude data; performing optimal control solution on the plurality of mooring points to obtain tension distribution control data of each mooring point; inputting into a discrete time neural network controller for attitude compensation calculation to obtain thrust vector compensation control data; performing adaptive robust control on the attitude of the mooring unmanned aerial vehicle to obtain a rotating speed control instruction of each motor of the four rotors; and performing safety threshold judgment, and when any monitoring data exceeds a preset threshold, outputting a working mode switching instruction and executing safe landing control. According to the invention, a multi-stage safety protection mechanism is established, and reliable operation of the mooring unmanned aerial vehicle under extreme conditions is ensured through real-time monitoring and mode switching.

The invention discloses a renewable energy power generation power prediction and power dispatching method and system, and the method comprises the steps: collecting the historical power generation data and real-time meteorological data of renewable energy power generation, carrying out the linear interpolation of the historical power generation data and the real-time meteorological data, and carrying out the missing value filling and box plot anomaly detection, obtaining a normalized training data set; constructing a hybrid prediction model by using the normalized training data set and adopting a neural symbol acceleration technology with time logic constraints, extracting medium and long term space time features, and generating a renewable energy power generation power prediction result; and according to the renewable energy power generation power prediction result and the system constraint condition, adopting a linear one-dimensional projection constrained distribution robust control method to formulate a scheduling strategy, and utilizing the scheduling strategy to solve an optimal scheduling scheme through mixed integer linear programming. According to the method, the renewable energy power generation power prediction precision and the power dispatching robustness are remarkably improved.

The invention discloses a data-based predefined time heterogeneous multi-agent formation collision avoidance method. The method comprises the following steps: establishing a nonlinear heterogeneous multi-agent system; designing a bimodal artificial potential field function to perform dynamic obstacle avoidance and prevent regional escape; establishing a self-adaptive robust controller used for generating an obstacle avoidance safety motion trail of the root leader; designing a self-adaptive formation zooming mechanism of the leader, and constructing a predefined time affine observer of the follower based on the self-adaptive formation zooming mechanism; designing a unified obstacle function; constructing a virtual control law for processing tracking errors based on the unified obstacle function; designing a neural network estimator; designing controllers of the leader and the follower according to the virtual control law; and forming a collision avoidance decision of the heterogeneous multi-agent formation based on a self-adaptive robust controller, a predefined time affine observer, a neural network estimator and controllers of the leader and the follower. According to the method, safe, efficient and robust cooperative control of the formation in a complex environment is realized, and the safety and task execution efficiency of the heterogeneous multi-agent formation in a limited and unknown environment are remarkably improved.

The invention discloses a four-rotor unmanned aerial vehicle model prediction control method based on a deep Q-network (DQN). The method comprises the following steps: firstly, establishing an inertial coordinate system and a body coordinate system, and establishing a kinematic equation and a kinetic equation of the four-rotor unmanned aerial vehicle based on a Newton second law and an Euler-Lagrange equation; according to the method, an unmanned aerial vehicle robust controller based on model predictive control (MPC) is designed, optimal control input is generated through dynamic optimization, and the trajectory tracking performance of the unmanned aerial vehicle is improved. For various disturbances existing in the whole tracking process, a DQN reinforcement learning algorithm and an MPC method are combined to design a flight control system, control rate errors caused by the disturbances are compensated, the stability of attitude control is enhanced, and the tracking precision and the anti-interference capability are improved. And finally, verifying the robust performance of the flight control system through a simulation experiment.

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 provides a distributed steer-by-wire vehicle stability control method. The method comprises the steps that a distributed steer-by-wire vehicle dynamics model, a two-degree-of-freedom reference model and a four-wheel steering angle distribution model are established; in the design of the yaw stability controller, a hybrid sensitivity # imgabs0 # robust control method is adopted, the uncertainty of the system is fully considered, and the robustness of the system can be considered while the target tracking performance is ensured.

The invention discloses an adversarial reinforcement learning training method for robust control of a fixed-wing aircraft, and the method comprises the steps: constructing a nonlinear simulation environment and a task set of the fixed-wing aircraft, and constructing a plurality of fixed-wing aircraft agents in parallel in the simulation environment; an adversarial agent module based on a neural network AdvNet is introduced to generate environmental disturbance. A near-end strategy optimized Actor-Critic structure is adopted, an intelligent agent and an adversarial agent module are subjected to joint training through a task set, and a robust control strategy is obtained step by step. In the training process, based on a course learning mechanism, disturbance constraint parameters of the confrontation agency are dynamically adjusted according to the completion condition of a flight task. After training convergence, obtained control strategy parameters are applied to the fixed-wing aircraft, and robust flight control is achieved. According to the method, the robustness and the control precision of the aircraft facing complex environment disturbance can be improved, and the method is suitable for various flight missions with uncertainty.

The invention discloses a data center machine room energy-saving optimization method and system based on thermal environment prediction. The method comprises two stages of offline modeling and online prediction optimization. In the off-line stage, a CFD simulation model is constructed based on a machine room physical structure, equipment layout and thermal load parameters, and high-precision temperature field data is generated; using the thermal environment prediction model to input equipment parameters and load change to output future space temperature distribution; meanwhile, an XGBoost hybrid energy consumption prediction model is constructed based on the wind speed ratio or the fan frequency, the number of running fans and related characteristics. In the online stage, the lowest energy consumption and the minimum temperature deviation serve as targets, and a Pareto optimal solution set is generated through an MOEA / D algorithm; the optimal wind speed ratio / frequency and equipment number combination is selected as required to control operation of the air conditioner; and dynamically updating model parameters by sliding a time window to realize long-term robust control of the system. According to the method, intelligent energy-saving control of the machine room is realized by fusing CFD simulation, time sequence prediction, energy consumption modeling and a multi-objective optimization algorithm.

According to the step motor sensorless control method based on improved robust Kalman filtering, an HBM current state equation is established, and an extended Kalman state equation is established; establishing an inductance deviation model, and deducing a system covariance matrix with errors; parameter uncertainty robust control is introduced, an expansion factor is designed, and a system covariance matrix is reconstructed for limitation compensation; the method comprises the following steps: constructing chi-square test statistics through an innovation sequence, identifying a system gross error, designing a robust weight function based on a system residual vector, selecting whether to carry out variance correction or not according to an identification result, inhibiting the cumulative influence of a model mismatch error on state estimation, updating a state, and adjusting robust intensity. According to the method, parameter uncertainty robust control and an IREKF algorithm based on innovation detection are introduced to carry out dynamic compensation on a model mismatch error, REKF is improved through innovation detection, and the robust intensity is dynamically adjusted.

The invention discloses a power grid broadband oscillation suppression method and device based on adaptive robust control, terminal equipment and a storage medium, and belongs to the field of power systems. The method comprises the following steps: performing fuzzy disturbance classification according to an oscillation mode, an oscillation mode characteristic parameter and a state parameter obtained according to a power grid operation parameter to obtain a disturbance type label and a disturbance degree; the disturbance degree serves as a weighting factor, the initial control parameters are corrected according to the power grid operation parameters, the initial control parameters of the controller and the disturbance type label, and a self-adaptive control strategy is generated after correction; when the disturbance degree is smaller than a disturbance threshold value, the state parameters are regulated and controlled according to a self-adaptive control strategy to update power grid operation parameters; and when the disturbance degree is not less than a disturbance threshold value, taking the minimum sum of the system gain, the frequency deviation and the voltage fluctuation as a target, and regulating and controlling the state parameters under the H infinite robust constraint to update the power grid operation parameters. The problem that in the prior art, broadband oscillation suppression is difficult to adapt to sudden change working conditions is solved.

The invention discloses a multi-mode intelligent tension self-adaptive control method, and belongs to the technical field of tension control of wire rod processing devices. According to the method, an LSTM-Transform hybrid model is utilized to realize the collaborative optimization control of tension prediction, the rotating speed and the pitch; by means of an edge computing architecture and a Modbus-TCP real-time communication protocol, a data processing and control instruction generation period is compressed to be within 50ms, and the dynamic response capability of a system is improved; and meanwhile, a graded safety protection mechanism is designed, a robust control mode is switched to at a millisecond level when vibration energy exceeds a limit or the temperature is abnormal, and the MES system is linked to push a fault code, so that the control precision is remarkably improved, the material loss rate and the operation and maintenance complexity are reduced, and a high-reliability and self-adaptive intelligent solution is provided for high-speed wire rod processing equipment.

The invention provides a power distribution network optimization method based on hierarchical robust control and dynamic decision, which realizes multi-time scale coordination control and real-time adaptive control by constructing a hierarchical architecture of a minute-level equipment layer, an hour-level region layer and a day-preceding-level system layer and combining a dynamic information decision mechanism. The method comprises the steps of preprocessing measurement information in a grading manner, establishing a multi-target robust optimization model, decomposing the model into a local quick response and global coordination problem, constructing a time-varying uncertainty set and embedding mixed integer robust optimization, and realizing the collaboration of millisecond-level correction and hour-level scheduling by adopting a second-order cone relaxation technology. And the weight is dynamically adjusted and optimized through the information decision module, so that the robustness and the operation efficiency of the system are improved.

The invention discloses a reinforcement learning-based quadrotor unmanned aerial vehicle attitude preset performance control method, which comprises the following steps of: establishing an unmanned aerial vehicle attitude kinetic equation by considering a time-varying inertial parameter and external unknown interference; a sub-channel preset performance controller is constructed, and a robust control quantity is generated in combination with preset performance and a non-singular fast terminal sliding mode control technology; designing a reinforcement learning parameter generator, and dynamically optimizing 12 time-varying parameter estimated values through a time sequence feature extraction network and a residual network; and establishing an online reinforcement learning training mechanism, constructing a multi-target reward function, and optimizing the output of the parameter generator through the multi-target reward function. According to the method, a reinforcement learning method is adopted to replace a traditional self-adaptive method to estimate time-varying parameters, multi-degree-of-freedom decoupling optimization is achieved through a sub-channel control architecture, tracking error preset performance constraint is achieved under the working condition of time-varying inertial parameters, and the dynamic adjustment capacity and anti-interference robustness of an attitude system are remarkably improved.

The invention discloses a hydraulic mechanical arm motion control method based on low-pressure volume elastic modulus compensation. The method comprises the steps that a dynamic linearization model of the multi-degree-of-freedom hydraulic mechanical arm containing the effective volume elastic modulus is established, a backstepping method is used for designing a self-adaptive robust controller, an expected joint angle is input into the self-adaptive robust controller, meanwhile, online self-adaptive parameter updating is carried out, and the optimal joint angle is obtained. The self-adaptive robust controller outputs virtual control flow of the multi-degree-of-freedom hydraulic mechanical arm after processing, and valve port control voltage is obtained after static mapping so as to control a hydraulic valve; and the multi-degree-of-freedom hydraulic mechanical arm outputs the actual control flow, the actual joint control torque and the actual joint angle to the self-adaptive robust controller in real time, and motion closed-loop control over the multi-degree-of-freedom hydraulic mechanical arm is achieved. According to the method, the high-precision motion tracking performance can be achieved under the condition that the hydraulic mechanical arm keeps stable low pressure.

The invention provides a self-adaptive fractional order nonsingular terminal sliding mode control method based on time delay estimation, and relates to the technical field of manipulator control. Designing a non-singular terminal sliding mode surface containing fractional differential and power transformation, and further constructing a reaching law with variable gain; then, the unknown dynamic state and disturbance of the system are compensated in real time through a time delay estimation technology by utilizing historical control input and state information; finally, a control law is generated by integrating the sliding mode surface, the reaching law and the time delay estimation compensation item, high-precision and strong-robustness control over an uncertain nonlinear system is achieved, and the tracking precision and the anti-interference capacity of the manipulator in the uncertain environment are effectively improved.

The invention provides a robust control automatic setting method for a constant-pressure water supply system, and the method comprises the steps: obtaining a system working condition in real time, and extracting multi-dimensional state parameters including real-time flow, pressure deviation, current deviation and power deviation; the current load working condition is judged according to the pressure deviation, disturbance characteristics are analyzed by integrating all parameters, and the disturbance grade and type are determined. On the basis, robust control parameters are automatically set in combination with real-time working conditions, load conditions and disturbance characteristics of the system. And finally, performing multi-stage cooperative control on the system by using the set parameters, so that the constant-pressure water supply system is automatically set. According to the robust control automatic setting method for the constant-pressure water supply system, load working conditions and disturbance characteristics are recognized based on multi-dimensional state parameters, robust control parameters are self-set in a targeted mode, and automatic setting of the constant-pressure water supply system is achieved through multi-stage cooperative control. Therefore, the intelligent level of the constant-pressure water supply system and the adaptability in different operation environments are effectively improved.

The invention relates to the technical field of robot control, in particular to a high-precision double-closed-loop robust control method for a rope-driven super-redundancy mechanical arm, which is characterized in that dynamics modeling is performed on the rope-driven super-redundancy mechanical arm based on an equivalent moment method, control is divided into a joint space and a driving space, and a double-closed-loop controller is designed for the two spaces. In a joint space, a kinetic parameter adaptive estimation algorithm is designed, so that kinetic parameters converge to correct values within finite time, the accuracy of a dynamic feed-forward quantity is improved, and in a driving space, feed-forward compensation is performed on nonlinear dynamics of a motor, a sliding block, a guide rail and a rope in a driving module based on time delay estimation, so that the dynamic performance of the motor is improved. And sliding mode control is used as a feedback control law, the robustness of nonlinear friction and external disturbance of the rope is improved, and accurate and stable control over the rope-driven super-redundancy mechanical arm can be achieved.

The invention discloses a spraying equipment automatic control system based on the industrial internet, and belongs to the technical field of industrial automatic control. The system comprises a state sensing unit, a feature extraction unit, a risk prediction unit and a closed loop correction unit. The state sensing unit obtains a real-time working condition data set; the feature extraction unit generates a core process feature set; the risk prediction unit generates a quality risk prediction set; the closed-loop correction unit calculates a target process set point. The system senses multi-source data such as environment temperature and humidity and coating flow in real time, quantifies the influence of environment and process fluctuation on spraying quality, predicts indexes such as coating film thickness and defect probability in a prospective mode, and adjusts process parameters in a self-adaptive mode on the basis of working condition risk levels. Compared with the prior art, complex working condition insight, quality risk look-ahead prediction, dynamic intelligent optimization and extreme working condition robust control are achieved, the contradiction between high quality and high efficiency in automatic spraying is relieved, and the spraying quality stability and the production efficiency are improved.

The invention belongs to the technical field of aviation hybrid power, and discloses a self-adaptive intelligent power distribution control method for an aviation hybrid power system, which comprises the following steps: constructing a full-dimensional state model through multi-dimensional perception fusion, predicting working condition transition in combination with deep reinforcement learning, and dynamically adjusting a power distribution strategy; the response speed is guaranteed preferentially during emergency takeoff, energy recovery is emphasized during conventional landing, the problem that a traditional distribution mode is disjointed with actual requirements is solved, power output of an engine and a battery better fits the flight working condition, and stable operation can still be achieved in complex environments such as sudden airflow; by introducing digital twinborn rehearsal verification and adaptive robust control, risks of engine overtemperature, battery overcharge and the like are exposed and corrected in advance through virtual simulation, disturbance is dealt with in combination with a'basic + compensation 'power structure, part faults caused by unreasonable power distribution in traditional control are avoided, and the reliability of the system is improved. And the reliability of the hybrid power system in key stages of takeoff and landing is improved.

The invention discloses a tunnel pipe roofing construction dynamic deviation correction method based on laser ranging and optical fiber sensing, and relates to the technical field of measurement and sensing, and the method comprises the steps: S1, collecting laser and optical fiber data, obtaining real strain through temperature coupling compensation, and calculating global pose parameters; s2, generating an eigenmode component based on the real strain and the global pose parameter, and screening and constructing an intelligent feature vector according to the contribution degree of the physical coupling feature; s3, constructing a physical information driving state estimation model in combination with pipe joint mechanical constraints, and outputting a pipe joint state estimation value in real time; s4, performing construction deviation prediction by using the pipe joint state estimation value, and generating an optimal control sequence and instruction; and S5, applying the control instruction to the construction device, and adaptively updating the key parameters of the system through a meta-optimization algorithm based on feedback. And through multi-modal data fusion and physical constraint intelligent prediction, high-precision deviation correction, closed-loop robust control and system self-adaptive optimization of the pipe joints are realized, and the intelligence and long-term precision of tunnel pipe curtain construction are improved.

The invention relates to the technical field of hydropower station intelligent monitoring and dispatching, and discloses a hydropower station computer monitoring method based on domestic hardware, which comprises the following steps: dynamic system modeling: establishing nonlinear dynamic models, including water pump and generator power models, of equipment in a hydropower station, and describing the operation state of the equipment; the multi-objective optimization scheduling is used for calculating load scheduling of equipment based on a multi-objective optimization model of power consumption, equipment loss and system energy efficiency; and the fault prediction and early warning module is used for carrying out real-time prediction on the equipment state through Kalman filtering, carrying out fault mode prediction based on historical data and sending out fault early warning in time. According to the invention, through dynamic system modeling, multi-objective optimization scheduling, Kalman filtering fault prediction and H-infinity robust control, efficient scheduling, accurate fault early warning and robust control strategy optimization of hydropower station equipment are realized, and the operation efficiency and stability of the system are significantly improved.

The invention discloses a fuzzy neural network PID photovoltaic series welding temperature control method based on improved PSO optimization. The method comprises the steps that a target photovoltaic series welding equipment heating transfer function model is acquired; setting an initial PID parameter; adjusting a PID increment parameter of the PID control module in real time according to the temperature error, the temperature error change rate and a preset fuzzy rule base; the neural network is combined with fuzzy control, and parameters and rules of fuzzy control are automatically modified through training data; according to the photovoltaic series welding temperature condition, the fitness function of the PSO algorithm is improved, the adjustment mode of the inertia weight is improved, and the improved PSO algorithm is used for optimizing the initial PID parameters of fuzzy neural network PID photovoltaic series welding temperature control. Self-adaptive precise control and robust control of the welding temperature control system are achieved, the temperature fluctuation phenomenon in the photovoltaic series welding process is effectively improved, and the robustness and control reliability of the system are enhanced.

The invention provides an underwater robot dynamic positioning method for coping with complex disturbance, and the method comprises the steps: collecting the attitude angle, angular velocity, linear velocity and linear acceleration information of an underwater robot, and generating fused high-frequency and high-precision pose information; designing an extended state disturbance observer, and estimating a disturbance value in real time through a feedback function and an auxiliary variable equation; adding the disturbance estimation value and the output force of the dynamic model, constructing a constraint condition of model prediction control, and solving the optimal control input of N prediction time domains in the future through rolling time domain optimization; and based on the optimal control input, constructing a quadratic programming problem of thrust distribution in combination with the space pose distribution matrix of the thrusters, solving the minimum energy solution of the thrusters, and outputting the minimum energy solution to a thruster execution mechanism. According to the technical scheme of the invention, the robust control of the underwater robot in a strong disturbance environment can be realized. The method mainly solves the problem of fixed point and attitude determination when the underwater unmanned robot faces underwater turbulence or other complex disturbances.

The invention relates to the technical field of auxiliary driving, in particular to a front-wheel steering and rear-wheel distributed driving vehicle path tracking method. The method comprises the following steps: S1, establishing a nonlinear vehicle dynamics model of front-wheel steering and rear-wheel distributed driving; s2, establishing a path tracking task constraint; s3, designing an adaptive robust controller based on task constraints; s4, based on Lyapunov analysis, stability and constraint following error performance of the adaptive robust controller are analyzed; and S5, using the controller in the step S3 to control the vehicle to perform path tracking. According to the path tracking control algorithm provided by the invention, a model which better conforms to the actual dynamic characteristics of the vehicle is established, and the condition of relatively large initial tracking deviation is considered and processed, so that the control stability of the vehicle under the influence of uncertain factors and relatively large initial tracking deviation can be improved, and a tracking error sample is limited in a specified area.

The invention discloses a new energy electric power robustness control method, system and device for preventing chain overload of a power transmission section and a medium. The method comprises the following steps: obtaining the active power flow size and the operation state of each branch in the power transmission section; calculating the comprehensive sensitivity of all the controllable nodes to each branch in the power transmission section, and generating a composite priority index based on the calculated comprehensive sensitivity, the classification of the nodes and the new energy fluctuation risk; performing priority ranking on the generator nodes to generate a node sequence; in combination with a reverse equivalent pairing principle, selecting an adjustment node pair, and calculating an adjustment amount of node output by considering an adjustment amount dynamic margin constraint of new energy output uncertainty; recalculating the active power flow and the operation state of each branch in the section; and judging whether the section power flow and the margin meet a termination condition or not to realize optimal control. According to the method, the operation robustness of the new energy power system is improved, and the overload of the transmission section can be quickly and efficiently eliminated.

The invention discloses an under-actuated unmanned surface vehicle safe formation tracking control method based on robust obstacle control. The method comprises the following steps: establishing a multi-unmanned ship group and performing real-time communication, and enabling each follower to follow a leader according to a formation configuration and to avoid an obstacle; a safe formation tracking control framework of the underactuated unmanned surface vehicle is established, during formation tracking, followers input state information into the control framework, processing is carried out according to a reference trajectory of a leader and real-time information of a current follower and an obstacle, and forward and steering control torque instructions are output to control the current follower. Obstacles are avoided and formation configuration is restored to continue following the leader. According to the method, the underactuation characteristic problem of the unmanned surface vehicle is solved, the worst case error generated when the neural network approaches the unknown dynamic state can be effectively compensated, it is ensured that the unmanned surface vehicle can strictly avoid collision with the obstacle in the complex environment, and meanwhile the formation tracking performance is optimally kept.

The invention belongs to the technical field of robot motion control, and particularly relates to a quadruped robot control method based on error symbol robust integral feedback, which combines an RISE control mechanism with a constraint-based optimization method, and realizes high-precision trajectory tracking and strong-robustness control under the condition that a system model has relatively large uncertainty. According to the method, RISE controllers are designed in a position subsystem and a posture subsystem respectively, quadratic programming (QP) optimization distribution of ground contact force is fused, control input is dynamically adjusted while the physical feasibility of foot end force is ensured, and closed-loop stable control over the whole-body movement of the quadruped robot is achieved. The control strategy has an asymptotic error convergence characteristic, and can be widely applied to quadruped robot motion tasks with severe load change and frequent interference.

The invention discloses a high-frequency motor drive multi-rate control method for time sequence disturbance compensation, and the core principle of the method is that low-rate disturbance compensation and a reference tracking trajectory are improved and converted into a time sequence processing process matched with a high-rate control instruction; specifically, a single disturbance compensation value which can be known only at a sampling moment Ts is expanded into time sequence prediction of disturbance changes on N control points in the future through a generalized proportional-integral observer; meanwhile, a low-frequency single reference instruction is constructed into a smooth high-frequency reference trajectory covering N control points through a Lagrange interpolation method, and closed-loop correction of disturbance compensation and reference tracking is achieved on each high-frequency control point Tc. The problem that open-loop prediction errors are continuously accumulated and spread due to mismatching of information updating rates in traditional multi-rate control is fundamentally solved, and therefore high-precision robust control over the high-frequency motor driving system is achieved.

The invention discloses a trajectory tracking robust control method and system for a three-degree-of-freedom hydraulic mechanical arm, and relates to the technical field of automatic control. The method comprises the steps that a dynamic model is built for the three-degree-of-freedom hydraulic mechanical arm; according to the dynamic model and an output force model of the hydraulic valve control cylinder system, modeling is conducted on a hydraulic actuator, and a second-order model is obtained; designing a super-spiral sliding mode controller for the second-order model; determining an error angle between the expected joint angle and an actual joint angle of a joint in the three-degree-of-freedom hydraulic mechanical arm; inputting the error angle into a super-spiral sliding mode controller to obtain a control quantity; and according to the control quantity, the system uncertainty parameters and the external disturbance parameters, a hydraulic valve control cylinder system is controlled to move, and then a hydraulic actuator is driven to enable the three-degree-of-freedom hydraulic mechanical arm to move. According to the method, the system uncertainty and the external disturbance are hidden in the high-order derivative of the control law through the super-spiral sliding mode controller, the buffeting amplitude is remarkably reduced, the anti-interference capability is improved, and the robustness is kept.