638 results about "Dynamic equation" patented technology

The invention relates to the technical field of robot control, and discloses a mechanical arm trajectory planning control method and system based on a BAFARNN model. The method comprises the steps that a mechanical arm kinematics model is established, and a trajectory tracking problem is converted into a time-varying equation; designing a bounded adaptive function to activate a recurrent neural network model, defining an error function and constructing a dynamic equation; designing a piecewise adaptive coefficient function, and dynamically adjusting the gain according to an error norm and time; setting a Lissajous curve as an expected trajectory, and initializing a simulation environment; the joint speed is solved in real time through an ODE numerical method, and the mechanical arm is driven to move; actual motion data is collected and compared with an instruction, and closed-loop feedback control is triggered when the actual motion data exceed a threshold value. According to the method, rapid convergence is achieved through the piecewise adaptive coefficient function, the bounded activation function and the negative feedback mechanism are adopted to suppress noise, and high-precision and real-time trajectory tracking of the mechanical arm in the dynamic environment is achieved.

The invention discloses a power electronic networking equipment stability analysis method, system, equipment and medium, and the method comprises the following steps: building a full-system state space model, carrying out the cross validation of the full-system state space model through employing a modal analysis method, an impedance analysis method and a complex torque analysis method, and obtaining the stability of the full-system state space model; obtaining a multi-mode coupling stability analysis result; according to the multi-mode coupling stability analysis result, constructing a power grid s domain node admittance matrix; establishing a virtual power angle dynamic equation of the network-forming converter, and optimizing control parameters according to an evaluation result; an inertia center reference system is adopted, a dynamic interaction model of the synchronous machine and the network-forming converter is established, and dynamic coupling characteristics between devices are analyzed through a relative motion equation. According to the method, the comprehensiveness, the precision and the engineering applicability of dynamic stability analysis of the novel power system are improved through fusion of a multi-dimensional collaborative evaluation framework and an innovative technical system.

The invention discloses a hydraulic arm safety control method and device based on parallel learning and a high-order CBF, and the method comprises the steps: building a kinetic equation of a hydraulic mechanical arm through a Lagrange method, integrating unmodeled dynamics, structural parameter change and external interference into an uncertain item, and describing the generalized uncertainty of the uncertain item in a linear parameterization form; for the problem of insufficient excitation in a complex environment task, a parallel learning mechanism is introduced, and historical data and real-time data are combined to realize parameter identification. For high relative order safety constraints (such as obstacle distance constraints and joint limiting constraints) in a task space, an obstacle function family is constructed. A dynamic error buffer function is introduced, and a high-order adaptive control barrier function condition is designed. The high-order self-adaptive control obstacle function constraint is embedded into a real-time quadratic programming solving problem, input obtained through optimization solving can keep the track precision, meanwhile, a joint instruction is automatically corrected to prevent constraint failure, and minimum intervention type safety control is achieved.

The invention discloses a power system probability load prediction method, system and device, and a storage medium, relates to the technical field of hydrogen energy ship power system load prediction, and aims to solve the technical problems that multi-source data and physical information rules are not integrated and multi-scale chaos in an HPV complex operation environment is not considered in the prior art. The method specifically comprises the steps of collecting and preprocessing multi-source data; screening strong correlation data by using the maximum information coefficient, and reconstructing multi-source data into a unified spatial-temporal characteristic matrix; constructing an electrical and environmental parameter chaos model based on a Lorentz dynamic equation as a physical constraint term of a loss function; a BERT-PINN framework with an attention mechanism is established, and multi-step probability prediction is realized through two-stage training. According to the method, the data driving model and the physical information rule of the multi-source data are effectively integrated, the feature fusion problem of the multi-scale chaotic features is solved, and the model prediction performance is improved.

The invention discloses a reinforcement learning-based cluster spacecraft multi-target intelligent cooperative tracking method. The method comprises the steps of establishing a relative motion relationship between a tracking spacecraft and a target spacecraft through a nonlinear orbit kinetic equation; designing a double-layer game framework, constructing a non-zero sum game in the cluster to drive cooperation, and introducing a zero and maximum and minimum game between the cluster and a target to describe a confrontation relationship; based on communication topology and relative state information, defining a local error term and a performance index function of the tracking spacecraft and the target spacecraft, and defining an optimization target of each agent under cooperation and confrontation; and iteratively updating the value function and the control law. According to the invention, collaborative tracking can be realized without defining a system model. The method can be widely applied to the field of cluster control.

The invention discloses a confidence embedded model predictive control method, a medium and equipment, and belongs to the technical field of suspension control. According to the method, a kinetic equation of a suspension system is established, a data-mechanism dual-drive uncertainty quantification method is adopted, original operation data of a maglev train and suspension state information measured on line are utilized, and dynamic characteristic learning modeling is conducted on the suspension system under the constraint of the kinetic equation; state quantity distribution of suspension air gaps, vertical speed, electromagnet current and the like at future moments is predicted, and prediction mean values and prediction standard deviations of corresponding states are obtained. In model prediction control optimization, a nominal trajectory of the suspension system is constructed by a predicted state mean value, and a penalty term proportional to prediction uncertainty is added into a cost function; meanwhile, confidence coefficient constraint is constructed based on the predicted standard deviation, and dynamic constraint tightening of key physical quantities such as suspension air gaps, vertical speed and coil current is achieved. According to the invention, real-time sensing and self-adaptive closed-loop control of the operation risk of the suspension system are realized.

The invention discloses a fidelity simulation method and system for a mine catastrophe scene, and relates to catastrophe simulation. The method comprises the steps of obtaining mine disaster historical data; performing statistical analysis and feature extraction on the historical data by adopting a multi-dimensional correlation analysis algorithm to obtain a main control element type set of the mine disaster and a weight coefficient of each main control element; setting an underground catastrophe scene reproduction scheme of each main control element type according to the main control element type set; catastrophe process monitoring data are collected in the target simulation area; establishing a mapping relation model between the main control elements and the disaster characteristic parameters through a machine learning algorithm; and generating an arrangement scheme of the underground catastrophe simulation device according to the mapping relation model. For the problem that mine catastrophe simulation lacks physical mechanism constraints in the prior art, residual errors of a gas diffusion equation, a heat conduction equation and a fluid dynamic equation are introduced into neural network training to serve as physical constraint loss items, and the generalization ability of simulation under different geological conditions and mining modes is improved.

The invention provides a PINN-based natural epidemic disease transmission mechanism modeling method and system, which are used for mechanism deduction and space-time prediction of a natural epidemic disease transmission process. The method comprises the following steps: firstly, constructing a host population model and a host-population coupling propagation dynamics model; secondly, the dynamic equation set serves as a physical constraint to be embedded into the physical information neural network; and finally, through joint loss function optimization network learning, bidirectional driving of case data prediction and parameter inversion is realized. The method solves the problems that in the prior art, a host-crowd coupling mechanism is lacked, environmental factor modeling is insufficient, and data driving and mechanism modeling are separated, two-way driving of mechanism modeling and data learning is achieved, and space-time simulation precision, interpretability and model generalization ability are remarkably improved.

The invention discloses a GIS in-place robot coordination control method with a posture correction function, and belongs to the technical field of robot coordination control. The method comprises the following steps: establishing a 2D-3D coordinate mapping corresponding relation; fusing the corresponding relation with an operation state predicted by an IMU-based inertial measurement and system dynamics model; performing error calculation on the obtained 6D pose information and target pose information; constructing a nonlinear kinetic equation of the robot, and converting a control target into an optimization problem of various constraints; and converting the obtained control instruction into a torque signal for actually driving the robot to move. According to the method, the influence of mismatching on pose estimation is effectively inhibited by establishing the 2D-3D coordinate mapping relation; through a joint torque PID control algorithm, the torque required by each joint is calculated in real time, and high-precision tracking of an expected trajectory is realized; an MPC optimization control framework with multiple constraints is constructed, and precise, smooth and safe coordination control over the GIS in-place process is achieved.

The invention relates to a multivariable system control method based on causal reasoning and reinforcement learning, and the method comprises the steps: collecting the power, the flywheel rotating speed and the temperature of a flywheel energy storage system in the operation process through a multi-sensor group, and carrying out the preprocessing of the power, the flywheel rotating speed and the temperature, and obtaining a standardized feature vector; mining a causal relationship among the power, the flywheel rotating speed and the temperature through a three-layer residual network for the standardized feature vector, and constructing a causal graph; a flywheel kinetic equation is injected into the third-layer residual network; optimizing a control strategy of the flywheel energy storage system by adopting a reinforcement learning algorithm based on the causal diagram; and a cascade fault suppression strategy is adopted to correct the control strategy based on the stability of the flywheel energy storage system, the corrected control strategy is converted into an instruction, the instruction is transmitted to an actuator, and the control strategy is executed. According to the method, the causal missetup rate is reduced, strategy convergence acceleration is accelerated, and the service life of equipment is prolonged.

The invention belongs to the technical field of control, and discloses a water surface robot high-precision trajectory tracking control method and related equipment, and the method comprises the steps: constructing a dynamic multi-modal environment map containing water surface three-dimensional geometric topology information and water flow field vector information; according to the dynamic multi-modal environment map, an improved quantum particle swarm optimization algorithm embedded into a water flow kinetic equation constraint is adopted to carry out global path planning, smooth parameterization processing is carried out on a global path obtained through planning, and a reference trajectory is generated; according to the vector information of the water flow field, flow field disturbance is predicted based on a fluid-structure interaction dynamics model, system residual disturbance is estimated by using an extended state observer, and feedforward control quantity is generated in combination; according to the real-time flow velocity of the water flow field and the trajectory tracking error, a nonlinear model prediction controller and an adaptive sliding mode controller are adaptively switched, and a control instruction is generated in combination with a feedforward control quantity to drive the water surface robot to track a reference trajectory; therefore, high-precision trajectory tracking control is realized.

The invention provides an optimal primary frequency control method based on reinforcement learning. The method comprises the following steps: establishing a dynamic model of a power system; designing a frequency control strategy based on a deep reinforcement learning algorithm; the system frequency deviation is used as a reward signal, and a control strategy is trained through interaction with a power system; lyapunov function stability constraints are introduced into the reinforcement learning algorithm; a neural network is adopted to carry out parameterization design on the controller; discretizing the frequency dynamic equation, and proposing a multi-node collaborative optimization target; and deploying the trained control strategy to an actual power system, and adjusting control parameters in real time through an online optimization technology to adapt to the dynamic change of the system operation state. By means of the method, the frequency control accuracy and stability of the power system are improved, and the global optimal performance of frequency control of the whole power grid is achieved in actual deployment.

The invention discloses a pathology adaptive prediction control method for a lower limb rehabilitation exoskeleton robot, and belongs to the technical field of rehabilitation robot control. The method comprises the steps that the joint angle, the angular speed and the man-machine interaction torque of the lower limb rehabilitation exoskeleton robot are collected in real time, and a kinetic equation of the lower limb rehabilitation exoskeleton robot is constructed; on the basis of the kinetic equation of the lower limb rehabilitation exoskeleton robot, parameter vectors representing the current pathological features of the patient are estimated online, so that a prediction model in the lower limb rehabilitation exoskeleton robot is updated, and the optimal auxiliary torque is solved. The intelligent brain of the exoskeleton robot can be deeply fused with the real-time state of the body of the patient, and rehabilitation training which is really personalized and self-adaptive and can effectively promote neural function recovery is provided for the patient.

The invention relates to the technical field of education data analysis, and discloses a student development comprehensive evaluation method based on a multi-dimensional accomplishment index, and the method comprises the steps: obtaining the multi-dimensional performance data of a student, and constructing an N-dimensional state vector containing the position and speed; constructing an N-dimensional dynamic virtual potential energy field formed by superposing static potential energy representing an education target and dynamic potential energy reflecting individual history; simulating the evolution of an N-dimensional state vector in a field according to a kinetic equation in which dynamic inertial mass is introduced to generate a development trajectory; and finally, quantitatively analyzing the track characteristics, and outputting an evaluation result. According to the method, the historical intervention effect can be evaluated by comparing the actual trajectory with the reference trajectory, preferential selection can be carried out by simulating future trajectories under different interventions, and a basis is provided for formulating a personalized strategy. According to the method, the physical dynamics model is introduced into educational evaluation, dynamic, process and predictive evaluation is realized, static limitation of traditional evaluation is overcome, and evaluation is more scientific and objective and has prospective guiding significance.

The invention provides a time mark event triggering reinforcement learning multi-agent consistency control method and system, and relates to the technical field of multi-agent cooperative control. Establishing a multi-agent system dynamics model comprising a leader and a plurality of followers on the time mark; based on the state information of the leader and the follower, constructing a tracking error and consistency error dynamic equation between the intelligent agents; a reinforcement learning controller based on a neural network is constructed, and an optimal control strategy and a performance index function are approached; an event triggering mechanism is designed, and the controller is triggered to update only when the local error of the intelligent agent exceeds a dynamic threshold value; by constructing a Lyapunov function on a time scale and solving a linear matrix inequality condition, bounded consistency of the system is ensured, and a Zeno phenomenon is eliminated. Unified modeling and distributed safety control of the time scale multi-agent system in the non-ideal environment are achieved, communication resources are saved, the convergence speed is increased, and the robustness and practicability of the system are enhanced.

The invention is suitable for the field of portal frames, and discloses a portal frame dynamics analysis method which comprises the following steps: establishing a full-order finite element model of a portal frame, and obtaining a mass matrix and a stiffness matrix of the full-order finite element model; dividing the full-order finite element model into a plurality of substructures based on the modular structure characteristics of the portal frame, and decomposing the degree of freedom of each substructure into an internal degree of freedom and a boundary degree of freedom; constructing a fixed boundary main modal matrix and a constraint modal matrix of each substructure, and constructing a reduced-order basis matrix; performing modal polycondensation on a mass matrix and a stiffness matrix of the full-order finite element model by using the reduced-order basis matrix to generate a reduced-order mass matrix and a reduced-order stiffness matrix, and constructing a reduced-order kinetic equation; and solving the reduced-order kinetic equation to obtain reduced-order modal coordinates, carrying out inversion reduction in combination with the reduced-order basis matrix and the reduced-order modal coordinates to obtain a displacement response vector of the full-order finite element model, and retaining key kinetic information of the portal frame to the greatest extent through reduced-order efficiency improvement.

The invention discloses a full-coupling numerical simulation system and method for a wind and wave hybrid energy obtaining system, and relates to the field of data analysis, and the method comprises the steps: generating an aerodynamic load of a current period based on incoming flow wind speed and coupling dynamics response data of a previous simulation period; based on preset mooring system parameters and the coupling dynamics response data of the previous simulation period, generating a mooring load of the current period; in the random wave field, determining the hydrodynamic force of the floating foundation entity and the wave energy power generation entity, and generating a wave load; determining a PTO force based on the relative motion between the power generation platform and the wave energy power generation entity; and taking the aerodynamic load, the mooring load, the wave load and the PTO force as external force terms in the same time domain kinetic equation for unified solution, and obtaining a coupling kinetic response of the current simulation period. According to the full-coupling numerical simulation system and method for the wind and wave hybrid energy obtaining system, the simulation precision of the simulation system can be improved, and the overall calculation efficiency of the system is improved.

The invention discloses a multi-mechanical-arm system random predefined time control method based on event triggering, belongs to the technical field of control, is used for time control of a multi-mechanical-arm system, and comprises the steps that a kinetic equation of the multi-mechanical-arm system is introduced, a track error tracking system is introduced, and a consensus error of the multi-mechanical-arm system is obtained; an unknown function in a kinetic equation of the multi-mechanical-arm system is estimated, a virtual control signal, a self-adaption rate and an event triggering mechanism are designed according to a self-adaption backstepping method, and stability analysis is conducted in combination with the Lyapunov stability theory. An event triggering mechanism is adopted at the same time, the updating frequency of the controller is reduced, and consensus errors of the experimental multi-mechanical-arm system can be converged into a small neighborhood within predefined time of 1 s in combination with the consensus errors of the experimental multi-mechanical-arm system.

The invention discloses a power system transient stability evaluation scene expansion method. The method comprises the following steps: fusing a non-equilibrium thermodynamic sampling framework with a dynamic equation of a power system to obtain a physical constraint diffusion generation model; establishing a transient differential equation embedding mechanism of the power system; aiming at the space-time correlation characteristics of the dynamic trajectory of the power system, designing a space-time decoupling self-attention coupling mechanism based on a physical constraint diffusion generation model, deeply fusing the attention mechanism with the physical law of the power system, and establishing an interaction relationship through a dynamic equation; through a hierarchical decoupling strategy of system-level diffusion, node-level inverse diffusion and dynamic projection, a space-time decoupling self-attention coupling mechanism is fused to capture complex association, the rationality of a dynamic trajectory of a power system is guaranteed by relying on physical constraints, and scene expansion of global-local coordination and a physical constraint closed loop is realized. According to the method, the original data distribution characteristics can be effectively learned, the high-quality instability scene is synthesized, the original data set is balanced, and the transient stability evaluation performance is improved.

The invention discloses a quantitative characterization method for computing power-thermodynamic collaborative flexibility of a data center. The quantitative characterization method comprises the following steps: acquiring real-time state data of the data center; on the basis of a thermodynamic dynamic equation, a coupling constraint model containing computing power adjusting power, a task schedulable time window and chip temperature change is established, the coupling constraint model reflects dynamic responses of different cooling modes by introducing thermal inertia parameters, and a temperature safety interval and a temperature change rate upper limit are set as hard constraints; based on the coupling constraint model, the maximum up-regulation flexibility and the maximum down-regulation flexibility which can be provided by the data center under the condition that the temperature safety constraint is met are calculated respectively, and adjustable IT load power ranges corresponding to different durations are obtained; and integrating the up-regulation flexibility and the down-regulation flexibility into a collaborative representation result containing a time-power-temperature three-dimensional relationship, and outputting the collaborative representation result in a structured parameter set form. According to the invention, the structured measurement of the flexible resources of the data center is realized.

The invention provides an anti-saturation fault-tolerant control method and device for a double-flexible-arm space robot, relates to the field of robot control, and solves the multiple challenges that in the prior art, a flexible mechanical arm faces continuous and difficult vibration suppression, an actuator is prone to failure, torque is limited, task time is constrained and the like. The dynamic state of the system is unstable; and the control capability is limited. The method comprises the steps that a hypothesis modal method and a momentum conservation theorem are combined to derive a double-flexible-arm space robot kinetic equation in a Lagrange form; a flexible system singular perturbation technology is utilized to obtain a slow-varying subsystem for representing rigid motion characteristics of a mechanical body and a quick-varying subsystem for representing residual vibration characteristics of a rod piece; for the slow-varying subsystem, designing a fixed-time anti-saturation fault-tolerant controller based on an extreme learning machine and a torque output function; and designing an anti-saturation controller based on state variable negative feedback for the quick change subsystem. The method is used in the robot control process.

An anti-disturbance control method for a cable-driven flying robot. The method comprises: 1. calculating a desired trajectory of a traveling mechanism, a joint angle of a robotic arm, a desired trajectory of the center of an unmanned aerial vehicle platform and an initial position of the traveling mechanism; 2. calculating a disturbance force and a disturbance torque exerted by the motion of the robotic arm on the unmanned aerial vehicle platform, and calculating a disturbance force and a disturbance torque induced by a cable, so as to obtain a total disturbance equation; 3. establishing a dynamic equation of a flying robot system, performing decoupling design to obtain a decoupled dynamic equation, and obtaining calculation formulas of a rotor lift, a rotor torque, a desired pitch angle value and a desired roll angle value; 4. constructing a system state error dynamic equation, and calculating the rotor lift and the rotor torque of a flying robot; and 5. designing a rotor rotational speed allocation matrix, and calculating a rotor rotational speed on the basis of the rotor lift, the rotor torque and the rotor rotational speed allocation matrix, so as to realize the anti-disturbance operation of the flying robot. The present invention solves the problems of stability and safety during the operation of a flying robot.

The invention belongs to the related technical field of automatic control, and discloses an active-disturbance-rejection control method and device for dynamic trajectory tracking, and the method comprises the steps: (1) calculating a system tracking error between an output signal of a controlled object and a reference trajectory, and building an error dynamic equation of the system tracking error; (2) reconstructing the error dynamic equation into an integrator cascade standard form through composite lumped disturbance, and regarding the composite lumped disturbance as a to-be-estimated expansion state of the integrator cascade standard form; (3) designing a tracking and guiding type extended state observer, and estimating the extended state vector in real time and outputting a corresponding estimated value by the tracking and guiding type extended state observer by taking a system tracking error and a control input signal as input; and (4) designing a compensation control law based on the obtained estimated value to generate a control input signal, and stabilizing an error system corresponding to the integrator cascade standard form by using the control input signal. According to the method, the problem of target non-equivalence is fundamentally solved.

The invention relates to the technical field of power transmission line engineering, and discloses a power transmission line vibration and sag analysis method considering a wind load, and the method comprises the steps: obtaining the meteorological data of a geographic region through which a power transmission line passes, and building a wind load model; establishing a kinetic equation of the transmission conductor; based on the wind load model and the kinetic equation, a numerical calculation method is adopted to solve the vibration response of the transmission conductor; calculating sag change conditions of the transmission conductor under different working conditions based on a vibration response calculation result of the transmission conductor; the system analyzes sag calculation results of the transmission conductor under different working conditions, and comprises a data acquisition module, a dynamics calculation module, a sag calculation module, a safety evaluation module and an optimization module. According to the method, the nonlinear distribution function that the wind speed changes along with the height is introduced into the wind load model, the effect of more accurately simulating the wind load under the complex meteorological condition is achieved, and the simulation of the wind load is more reliable and close to the reality.

The invention discloses a consistency control method and system for second-order multi-agent iterative learning and a medium. The method comprises the steps that a second-order nonlinear CMANSs kinetic equation which is composed of N follower bodies and a leader body and has uncertainty disturbance is constructed; constructing a double-layer adaptive iterative learning control input model; controlling an input model based on the double-layer adaptive iterative learning so as to enable the tracking consistency error of the iterative learning of the second-order nonlinear CMANSs kinetic equation within a preset time range to be within a preset range; and on the basis of a Lyapunov function, the tracking consistency error of iterative learning in a preset time range is demonstrated in a preset range. Therefore, the method can control the input model through double-layer adaptive iterative learning, and ensures that the second-order nonlinear CMANSs can still realize robust tracking in an uncertain environment, thereby realizing tracking consistency of the second-order nonlinear CMANSs with uncertain disturbance in finite time.

The application discloses a kind of based on evolution game model personal carbon transaction dynamic incentive method and system, belong to intelligent traffic and carbon inclusive technology field.The method includes: obtaining the current trip strategy distribution state of traveler group;Evolution prediction model based on replicator dynamic equation is constructed, and the dynamic evolution of group trip strategy is described by coupling the payment function of carbon transaction income;With the evolution prediction model as kernel, by solving model predictive control optimization problem, the current optimal carbon transaction dynamic pricing coefficient is calculated and incentive is executed;Using the error of actual data and predicted value, the model parameters are corrected on-line feedback.The application combines replicator dynamic equation with model predictive control, realizes the accurate prediction and foresight guidance of group behavior evolution trend, has strong robustness, solves the problem that existing technology incentive strategy lags behind, model is easy to fail, can be widely applied to urban traffic demand management and carbon inclusive mechanism construction.

A method and system for joint torque compensation in a remote teleoperated arm is disclosed. For each joint of the teleoperated arm, a friction torque prediction model, nominal dynamic equation, and compensation residual prediction model are constructed using the arm's historical joint command torque, joint angular velocity, and joint temperature. During each real-time control cycle of the teleoperated arm, real-time compensation calculations are performed using the constructed models to obtain a reference feedforward compensation torque. The end effector force deviation of the teleoperated arm is calculated and mapped to a joint torque increment. A feedback fine-tuning compensation torque is generated based on the joint torque increment. The total compensation torque is calculated based on the reference feedforward compensation torque and the feedback fine-tuning compensation torque. The final torque command for each joint servo driver is calculated based on the total compensation torque. This invention systematically solves the problems of model accuracy, dynamic response, and disturbance suppression, thereby achieving a truly high-fidelity force perception and presence.

This invention provides a method for reconstructing actuator faults in a heterogeneous multi-agent integrated energy system, relating to the field of fault diagnosis. The invention establishes a unified dynamic model for the agents; defines intermediate variables and constructs their dynamic equations; builds a global error dynamic system for distributed intermediate observers and agents; derives the stability criterion for the global error system based on Lyapunov stability theory, obtaining the state observer gain matrix for state estimation; loads the state observer gain matrix and the gain matrix for state estimation into each agent; and during the real-time operation of the agents, obtains the current state vector estimate and intermediate variable estimate, and calculates the reconstructed actuator fault value. This invention utilizes an output space coupling mechanism, enabling distributed collaboration between different energy networks without requiring a unified model dimension, thus breaking down the physical barriers to actuator fault reconstruction in integrated energy systems.

The invention discloses a train rail coupling vibration control method, device and equipment and a readable storage medium, and is applied to the technical field of intelligent control, and the method comprises the steps: obtaining a nonlinear model considering a vibration mode of a rail based on a high-speed maglev train structure of a lap joint structure, a rail vibration equation and an electromagnet kinetic equation; determining motion characteristics of the nonlinear model to obtain a motion state space equation; determining a reference model corresponding to the nonlinear model, and determining a control law when the difference between the motion state space equation and the reference model is minimum; and determining target vibration control parameters by using a control law by taking the nonlinear model as a control object reference model as an ideal target. According to the method, the nonlinear model considering the vibration mode of the track is obtained through the high-speed maglev train structure based on the lap joint structure, the corresponding reference model and control law are designed, and due to the fact that the vibration mode of the lap joint structure is considered in the nonlinear model, the suppression effect on coupling vibration of the high-speed maglev train track is better.

This invention discloses an adaptive impedance control method and device for a teleoperated robot system. The method is based on a fuzzy logic-based intelligent control mechanism. It dynamically generates a correction update rate for online compensation adaptive terms through fuzzy inference, based on real-time acquired master-slave state information and preset control parameters. This correction update rate, real-time state information, and preset parameters are integrated, and the core dynamic equations of the preset impedance control model are solved to calculate the robot's target position within the current control cycle. This method, based on the robot following the operator's hand movements, superimposes a position compensation amount optimized by the adaptive impedance control model. When problems such as operator hand tremor or network transmission delay cause a sudden increase in contact force, making it impossible to effectively track the desired contact force, the adaptive impedance control device adjusts the contact force, achieving refined teleoperation control.