417 results about "State space equation" patented technology

The invention discloses a space robot finite time anti-interference trajectory tracking control method based on performance constraint and actuator saturation, and the method comprises the steps: firstly solving a state-space equation of a flexible joint free flight space robot, constructing a system error vector in combination with a finite time instruction filter and an anti-saturation auxiliary system, and carrying out the finite time instruction filter and anti-saturation auxiliary system; a novel asymmetric logarithmic obstacle Lyapunov function with an asymmetric fixed time constraint boundary is constructed; and finally, a novel finite time advanced activation anti-saturation auxiliary system and a finite time adaptive controller with a performance constraint effect are constructed, so that finite time anti-interference trajectory tracking control of the space robot is realized. According to the method, the influence of sudden disturbance is considered in the design of the flexible joint free flight space robot controller for the first time, and under the limitation of the saturation performance constraint of the actuator, the trajectory tracking rapidity and accuracy of the space robot are ensured; and the influence of sudden disturbance in a space robot system on the trajectory tracking transient performance can be effectively inhibited.

The invention relates to an automatic guided vehicle (AGV) adaptive path tracking method based on MPC. The method comprises the following steps: S1, establishing a kinematic discretization error model based on the kinematic characteristics of the two-wheel differential AGV, processing a continuous kinematic equation by adopting an Euler discretization method, expressing a dynamic change relationship between a transverse deviation distance and an angle deviation in a state-space equation form, and generating a kinematic discrete state-space model; the method has the advantages that the continuous equation is processed by establishing the kinematics discretization error model and adopting the Euler discretization method, the deviation dynamic relation is expressed through the state space, the calculation process is simplified, the precision is kept, sensor data are fused, the wheel type odometer, IMU and laser data are integrated through the extended Kalman filtering algorithm, and the precision is improved. The real-time position and angle deviation are calculated, the positioning accuracy is improved, a model prediction controller objective function is designed, a weight matrix and boundary constraint are introduced according to constraint conditions, and the effect of obstacle avoidance constraint is combined.

The invention discloses a rope-driven mechanical arm motion control method based on model predictive control, which comprises the following steps: taking a joint angle error as a state variable, taking a rope-driven rotation angle as an input variable, and establishing a nonlinear state-space equation of a rope-driven mechanical arm according to the state variable and the input variable; the nonlinear state-space equation is subjected to linearization processing, a linear approximation model is generated, and the linear approximation model comprises a linearization matrix; defining a prediction interval, expanding the linear approximation model into a multi-step prediction form, and generating a prediction model; constructing a cost function, and combining the prediction model into the cost function to solve and obtain an optimal control sequence; and extracting the optimal control increment at the current moment from the optimal control sequence, and updating the state variable, the input variable and the linearization matrix according to the optimal control increment to form closed-loop control. Accurate path tracking control can be carried out on the rope-driven mechanical arm under complex dynamic constraints, and it is ensured that the mechanical arm moves according to the planned trajectory.

The invention discloses a coating machine unwinding tension control method which is characterized by comprising the steps that S1, parameters, operation data and tension data of a coating machine are collected, and a state-space equation of an unwinding tension control system is established; s2, constructing an obstacle function; s3, combining the obstacle function and constructing a non-singular sliding mode surface function; s4, solving an equivalent controller for controlling tension according to the non-singular sliding mode surface function; s5, optimizing key parameters of the equivalent controller by using an aurora optimization algorithm; s6, designing a self-adaptive control rate for improving the anti-interference capability of the unwinding tension control system; the adaptive control rate is combined with the equivalent controller, and a total control signal is output; s7, performing unwinding tension control on an unwinding tension control system according to the master control signal; according to the scheme, the speed of dynamic response can be adjusted, finite time convergence is enhanced, steady-state errors are eliminated, singularity is avoided, convergence is accelerated, and therefore high-precision control over an unwinding tension control system is achieved.

The present disclosure discloses a method for attitude control of a quadrotor UAV, comprising establishing an attitude dynamics model of the quadrotor UAV, establishing a motion equation and a state-space equation of a UAV control system, determining an LADRC-CFO, and establishing a differential tracker for reducing a system overshoot.

The invention belongs to the technical field of industrial equipment health monitoring and predictive maintenance, and discloses a machine tool feed shaft precision degradation predictive maintenance method based on a continuously optimized Mamba network. According to the method, a vibration signal of a machine tool feed shaft is collected through a three-way vibration sensor, and a low-dimensional feature vector is constructed after baseline correction, filtering noise reduction and feature extraction of an auto-encoder. The method comprises the following steps: pre-training a Mama network model by using full life cycle data of a test platform bearing, capturing a state evolution law in combination with a dynamic parameter generation mechanism, and discretizing a state-space equation through a zero-order retention rule to realize efficient time sequence modeling. In actual monitoring, the model predicts a future feature sequence based on part of input data, calculates health indexes and compares the health indexes with multi-stage thresholds, and four-stage state judgment of health / sub-health / initial degeneration / serious degeneration is achieved. A continuous learning mechanism is further introduced, model parameters are dynamically updated through experience playback and small learning rate fine adjustment, and working condition changes and novel fault modes are adapted.

An attitude control method for a spin detection satellite, which method belongs to the field of satellite attitude control. The method comprises: S10, establishing a satellite dynamic equation and converting same into a state-space equation; S20, designing a two-layer linear extended state observer, wherein the first-layer linear state observer is used for observing and acquiring a current angular velocity estimate and a current disturbance estimate of a satellite, and the second-layer state observer observes a tracking error of the first-layer linear state observer on the basis of the observation result of the first-layer linear state observer; and S30, then performing calculation on the basis of the current disturbance estimate and the tracking error thereof, so as to obtain the total internal and external disturbance torque estimate of the satellite, using attitude parameters representing the direction of a spin axis to design a PD control law, and compensating for the total internal and external disturbance torque estimate of the satellite, so as to calculate a control torque, which is used for satellite attitude control within a current cycle. The method has the advantages of fewer tuning parameters, ease of adjustment and stable performance, thereby greatly improving the observation precision.

The invention provides a delay compensation control method for a double-vibrator wave energy conversion device, which comprises the following steps of: performing numerical modeling and hydrodynamic analysis on the double-vibrator wave energy conversion device, establishing a time domain motion equation of the double-vibrator wave energy conversion device, and introducing a time delay function into the time domain motion equation to simulate control signal transmission delay; simulating the execution delay of the brake by using a partial differential equation; establishing a state-space equation of the device, replacing a convolution term of the time-domain motion equation with the state-space equation, and calculating to obtain a motion state of the device; a Hamiltonian function is defined to convert a constrained optimization problem into an unconstrained optimization problem, and the Hamiltonian function is solved to obtain an optimal control criterion considering control delay so as to realize maximization of energy capture under the optimal control criterion. According to the method, the operation characteristics of a physical system are truly reflected by introducing control delay, so that the locking control method is effectively implemented in an actual physical device, and the energy capture efficiency of a wave energy conversion device and the reliability of system operation are effectively improved.

The invention discloses an aircraft active side lever force feedback control method based on feedback linearization. The method is suitable for pitch axial force feedback control and roll axial force feedback control of an aircraft active side lever. The method comprises the following steps: establishing a kinetic equation of a mechanical transmission system and a pitch axis motor or a roll axis motor, and establishing a state-space equation of an active side lever system according to the equation; designing a linear expansion observer, and estimating the thrust applied to the active side lever by the driver based on the linear expansion observer; and designing a feedback linearization control method, deducing a control law according to a state-space equation of the active side lever system, and introducing the estimated thrust applied to the active side lever by the driver into the control law to realize real-time compensation of external interference. Under the condition that the thrust of the driver cannot be measured, the thrust of the driver is obtained through the linear expansion observer, the thrust of the driver is compensated by designing the control law, and therefore the dynamic performance and the steady-state performance of the servo system are improved.

The invention relates to a precise time synchronization parameter tracking method based on alternating minimization Kalman filtering, and belongs to the technical field of time synchronization. According to the method, timestamp noise is introduced into a clock synchronization parameter observation equation, and a state-space equation of clock synchronization parameter tracking is established in combination with a double-state clock model; a Kalman filtering method is used for tracking clock phase offset and frequency offset, and in the updating stage of Kalman filtering, an alternative minimization method is introduced for estimating the variance of timestamp noise and unknown observation noise in real time and correcting and updating Kalman gain. And furthermore, the influence of timestamp deviation and observation noise with unknown statistical characteristics on the clock synchronization parameter tracking precision is suppressed, and long-term effective tracking of the clock synchronization parameter is realized. According to the method, the problem that Kalman filtering is easy to diverge under unknown timestamp noise and observation noise is solved, joint tracking of clock phase offset and frequency offset can be realized, and the robustness and stability of clock synchronization are further improved.

The invention discloses a distributed driving electric vehicle path tracking and stability control method and system, and belongs to the technical field of electric vehicle active safety control. The method comprises the following steps: constructing a state-space equation based on a driver path tracking model and a vehicle three-degree-of-freedom motion model; establishing a dynamic envelope region based on a phase plane method, inputting the current parameters of the vehicle into the dynamic stable envelope region, and outputting a weight; constructing a controller based on the state-space equation, and taking the weight output by the dynamic stable envelope region as the control weight of the controller; inputting the vehicle state variables and the vehicle parameters into a controller, and outputting a longitudinal driving torque, an additional yaw torque, an additional front wheel turning angle and a driver intention turning angle required by driving; the additional front wheel turning angle and the driver intention turning angle are superposed to serve as input of a steering motor; a torque distributor is designed to distribute a longitudinal driving torque and an additional yaw torque, and the distributed torques are used as given values of the four hub motors.

The invention discloses a thermal power generating unit coordinated optimization control method based on model prediction, and particularly relates to the field of thermal power generating unit coordinated optimization control, and the method comprises the steps: S1, collecting the real-time operation parameters of a thermal power generating unit through a sensor network, carrying out the preprocessing of the real-time operation parameters, and building a unit operation state database; s2, constructing a machine-furnace dynamic prediction model based on the operation state database, adopting a state-space equation to describe and predict the parameter trend in the next 60s, and verifying the precision; s3, an optimization objective function containing multi-parameter deviation and energy consumption items is constructed according to the prediction trend, the optimal control quantity is solved in a rolling optimization mode every 10 s, and the control quantity is constrained by a rated parameter range; according to the method, the dynamic prediction model of the machine furnace is constructed and combined with the rolling optimization algorithm, the parameter change trend is predicted in advance to realize multi-variable cooperative control, and the advantages of reducing the parameter fluctuation range and improving the unit operation stability under the variable load working condition are achieved.

The invention belongs to the technical field of artificial intelligence, and discloses an actuator fault performance compensation method for a wind power plant grid-connected interconnection system, and the method comprises the steps: building a dynamic model of a nonlinear interconnection system containing a wind power plant; then, in combination with an actuator fault, constructing a state-space equation of a nonlinear system introducing a neural network; a composite observer with a dynamic event triggering mechanism is constructed, and estimation of the system state, external interference and actuator faults is achieved; according to the output of the composite observer, constructing a sliding mode safety compensation controller for realizing disturbance and fault compensation, and further obtaining an equivalent controller and a switching controller; and constructing a system master controller according to the equivalent controller and the switching controller. According to the invention, comprehensive estimation of the system state, external disturbance and actuator faults can be realized, active compensation of disturbance and faults can also be realized, and the system control stability is improved.

The invention discloses a vibration analysis method, device, medium and equipment for a vehicle electric driving system, and relates to the technical field of electric digital data processing, and the method comprises the steps: analyzing excitation characteristics and gear engagement dynamic load parameters based on a motor electromagnetic excitation and gear system dynamics model, and taking the excitation characteristics and the gear engagement dynamic load parameters as a mechanical excitation coupling motor, gear, bearing and shell model; according to the method, a rigid-flexible coupling dynamic model is formed, after the rigid-flexible coupling dynamic model is converted into a state-space equation, multivariable time-domain data are obtained through nondimensionalization and piecewise linear method solving, and then the transmission efficiency value of vibration energy among a motor, a gear, a bearing and all parts of a shell is calculated based on excitation characteristic parameters, gear meshing dynamic load parameters and the multivariable time-domain data. And positioning a key noise transmission path of the vehicle electric driving system according to the transmission efficiency value. By comprehensively analyzing the vibration characteristics of the system, the NVH problem of high-frequency howling and the like under the high-speed working condition is effectively solved.

The invention provides a lithium battery parameter online identification method based on dynamic weight particle swarm optimization, and relates to the field of power lithium battery model parameter identification. According to the method, a lithium battery equivalent circuit model based on a second-order RC network is established, and a state-space equation is derived according to the Kirchhoff's current law and the voltage law; a discrete output model is obtained through Laplace transformation and discretization processing; constructing a recursive least square parameter updater with a forgetting factor, and jointly optimizing an initial parameter vector of the forgetting factor recursive least square and the forgetting factor by adopting a dynamic weight particle swarm optimization algorithm; and based on the optimized initial parameter vector and the forgetting factor, performing recursive least square updating with the forgetting factor, performing online identification on model parameters, and reversely deducing an equivalent parameter value in an original circuit. According to the method, the parameter identification precision can be remarkably improved, and the problem that data saturation parameter convergence is slow in a traditional recursive least square method is solved.

The invention discloses an energy storage battery SOC and SOH joint estimation method based on adaptive double extended Kalman filtering, and the method comprises the steps: constructing a second-order RC equivalent circuit model of an energy storage battery, and building a state-space equation of a battery state and a battery parameter; measuring the open-circuit voltage of the energy storage battery under different SOCs through a pulse discharge experiment, and fitting a polynomial function relationship between the SOC and the open-circuit voltage by using a least square method; establishing a battery capacity model and an internal resistance model for energy storage battery SOH estimation; the measurement noise variance is dynamically adjusted based on an improved Sage-Husa adaptive algorithm, and joint estimation of the SOC and the SOH of the energy storage battery is realized through an iteration mode. According to the method, a relatively large state estimation error caused by fixed measurement noise is avoided, self-adaptive adjustment of the measurement noise is realized, and the reliability and the accuracy of SOC and SOH joint estimation of the energy storage battery are improved.

The invention belongs to the technical field of lower limb rehabilitation, and discloses a man-machine coupling system intelligent cooperative control method and device based on deterministic learning. Establishing a state-space equation of the lower limb exoskeleton robot, defining a tracking error, and designing a self-adaptive neural network controller of the lower limb exoskeleton robot; defining an expected position state vector and a real position state vector of the walking robot, solving a position state vector error, and designing a self-adaptive neural network controller of the walking robot; the method comprises the following steps: constructing a time-varying linear matrix, verifying the exponential stable convergence characteristic of the time-varying linear matrix under a continuous excitation condition on the basis of an adaptive neural network controller, storing nonlinear dynamic knowledge of a man-machine coupling system, and constructing a learning controller by using the learned knowledge. According to the method, while the unknown dynamic state of the man-machine coupling system is accurately modeled, the continuous excitation condition is ensured, so that accurate and stable trajectory tracking control is realized.

The invention provides a small signal frequency domain impedance automatic construction method compatible with a network tracking type system and a network constructing type system. For a complex system connected with a network-following type or network-constructing type converter, a uniform state-space equation is used for representing the characteristics of the converter, and an impedance calculation framework based on mapping between a time-frequency domain and a continuous discrete domain is established. The implementation process comprises the following steps: firstly, deducing a state-space equation of a single element, performing linear approximation and time domain discretization at a stable equilibrium point to form an equivalent Norton model, then dividing the whole network into two subsystems, hiding internal electrical parameters of the subsystems through model aggregation, and finally obtaining an equivalent Norton model; and automatic conversion from a small-signal time domain state space model of a single element to a system frequency domain model is realized. In the method, the system impedance model can be automatically constructed only according to branch and node information, the operation is simple, the expansibility is high, and the method is suitable for a large-scale power grid with a complex structure.

The invention discloses an active disturbance rejection controller control method and device, and the method comprises the steps: receiving an actual output feedback signal of a controlled object and a historical output control signal of a controller based on a linear expansion state observer, calculating the total disturbance of a system in real time through a three-order state-space equation, and obtaining a disturbance estimation value; receiving the disturbance estimation value and the control input gain coefficient based on a compensator, calculating a dynamic compensation amount for offsetting the total disturbance of the system, and outputting the dynamic compensation amount to a signal synthesis node; a deviation signal of a set value signal used for defining the target state of a controlled object and a feedback signal actually output by the controlled object is received based on a controller, and an anti-interference basic control signal is generated in combination with the bandwidth of the controller; and subtracting the dynamic compensation amount from the anti-interference basic control signal based on the signal synthesis node to generate an active-disturbance-rejection control amount, outputting the active-disturbance-rejection control amount to a controlled object, and suppressing external interference and internal parameter drift of the controlled object.

The invention discloses a periodic disturbance compensation method and device for an input time delay system, equipment and a medium, and relates to the field of disturbance control, and the method comprises the steps: obtaining a state-space equation of the input time delay system; according to the state-space equation of the input time-delay system, converting the input time-delay system into a non-input time-delay system by adopting a time-delay compensation method; based on the error signal of the no-input time delay system, estimating the value of the to-be-determined parameter by adopting an internal model principle and a linear regression method to obtain an estimated value of the to-be-determined parameter; and determining a control input signal of the zero-input time-delay system by adopting the feedback controller according to an output signal of the zero-input time-delay system and the to-be-determined parameter estimation value so as to perform periodic disturbance compensation on the zero-input time-delay system. The feedback controller is adopted to perform feedback control on the control input signal according to the output signal of the system, and compensation of unknown time-varying periodic disturbance of the input time delay system can be realized.

The invention discloses a dynamic safety interval control method for a low-altitude aircraft, relates to the technical field of low-altitude flight control, and solves the technical problem that the aircraft cannot be effectively controlled in the prior art. The method comprises the following steps: 1, selecting a certain aircraft as a target aircraft, calculating a potential field force FAB generated by a front aircraft on the target aircraft, and defining a state-space equation of the target aircraft in combination with an acceleration requirement of the target aircraft; if the target aircraft is an unmanned aerial vehicle, skipping to the step 2, and if the rear aircraft is a manned aircraft, skipping to the step 3; 2, eliminating a state estimation error caused by the operation time delay of the target aircraft by using an operation time delay compensation method, and obtaining a state-space equation of the target aircraft under the time delay tau; 3, solving the state-space equation by a genetic optimization algorithm to obtain an optimal interval control parameter; the dynamic interval control model provided by the invention greatly improves the operation efficiency of the complex low-altitude fusion airspace on the premise of conforming to the aircraft interval standard.

The invention belongs to the technical field of robot cooperative control, and particularly relates to a distributed self-adaptive control method and system for cooperation of multiple mechanical arms, and the method comprises the steps: decoupling a pure internal force vector from the data of a six-dimensional force sensor at the tail end through pre-identified kinetic parameters; calculating a cooperative confrontation index according to the pure internal force vector and the relative position vector, calculating a self-adaptive internal force penalty weight through nonlinear mapping based on the cooperative confrontation index, establishing a state-space equation of the system based on a double-integral model, introducing an equivalent environment stiffness matrix, mapping a prediction error vector in a prediction time domain into a prediction internal force vector, and obtaining a prediction internal force vector; and a cost function containing the product of the self-adaptive internal force penalty weight and the predicted internal force vector is constructed, an optimal control increment sequence is obtained by minimizing the cost function, and a speed instruction is updated to drive the mechanical arm to move. According to the method, the robustness and the execution stability of the system are effectively improved, and the cooperation safety is guaranteed.

The invention relates to an automobile thermal management system control method based on multi-target model predictive control, and belongs to the technical field of automobile thermal management systems. The method comprises the steps of establishing a discrete state space model of the thermal management system, setting a state variable, a control variable, a disturbance variable and a state space equation of the discrete state space model of the thermal management system, and then establishing a multi-target cost function and constraint conditions with stability, safety and low energy consumption of the thermal management system as targets; and then, based on fuzzy control logic, combining with an actual working condition, dynamically calculating a weight of each cost in the multi-target cost function, and finally solving an optimization problem according to the multi-target cost function under the determined weight in a discrete state space model of the thermal management system to obtain an optimal control input. And the optimal control input is transmitted to an actuator to regulate and control the automobile thermal management system. The system can adapt to various working conditions and environments, battery safety and comfort of a passenger compartment can be guaranteed, and energy efficiency and the service life of the actuator can be both considered.

The invention provides a network construction wind storage system frequency modulation optimization method based on explicit model prediction control, and the method comprises the steps: obtaining the real-time state data of a wind storage system, the real-time state data comprises the energy storage working state, the system demand power, the wind power working state and the wind speed; determining a state variable, control input and interference input according to the real-time state data; obtaining an initial state space equation according to the action relationship of the state variable, the control input and the interference input on the wind storage system; discretizing the initial state space equation to obtain a discrete state space equation; obtaining a power tracking error prediction model according to the coefficient matrix of the discrete state space equation; obtaining an optimization control model according to the power tracking error prediction model in combination with the control quantity change range; and obtaining a wind power control strategy and an energy storage control strategy of the wind storage system according to the real-time state data and the optimization control model. The response efficiency, continuity and accuracy of frequency modulation of the wind storage system can be improved.

A trajectory planning method for a lower limb exoskeleton includes: determining a state space equation corresponding to the lower limb exoskeleton using dynamic analysis; determining an objective function for trajectory planning of the lower limb exoskeleton according to the state space equation, wherein a phase variable corresponding to the objective function is a time-related phase variable; and performing trajectory planning of the lower limb exoskeleton according to the objective function to obtain a first gait trajectory corresponding to the lower limb exoskeleton, wherein the first gait trajectory is a gait trajectory corresponding to the time-related phase variable.

The present disclosure provides a cooperative distributed model predictive control (Co-DMPC)-based chassis multi-agent system (MAS) cooperative control method for autonomous vehicles, a controller, and a storage medium. A distributed state-space equation with state coupling and control input coupling characteristics is established. Meanings and transformation methods of predicted trajectories, assumed trajectories, and optimal trajectories of the states and control inputs are designed, providing a communication basis for information exchange between the agents. In order to coordinate the global performance indexes of a vehicle, a local agent optimization problem considering cost coupling is established, and the influence of the cooperative relationship on the control effect is quantitatively analyzed through adaptive weight coefficients. A method of performing a plurality of iterations within a unit sampling time is adopted, and iteration errors are utilized to enable the controller to achieve a balance between solution accuracy and efficiency.

The invention discloses a suspension pitching cooperative control system and method combining dynamic vertical force distribution and multi-mode braking, and particularly relates to the field of vehicle suspensions. Meanwhile, the braking torque distribution unit and the suspension system are controlled in real time by defining a state machine; vehicle low-speed target braking torque and tire vertical force are calculated through the expected deceleration and correspondingly input into a braking torque distribution unit and a four-degree-of-freedom suspension model; converting the four-degree-of-freedom suspension model into a state-space equation and discretizing the state-space equation to obtain a prediction model, and further deriving a state prediction equation of the future N steps; designing a cost function comprising a state quantity weight matrix Q, an input quantity weight matrix R and a state final value weight matrix F, substituting a prediction state into the cost function to obtain an optimal input sequence, dynamically adjusting suspension rigidity, damping and a braking force distribution strategy, and correcting a control instruction in real time by using closed-loop feedback of a vehicle body attitude sensor, so as to improve the stability of a vehicle body. And the suspension pitching control is realized.

The invention discloses a grid-connected inverter control method based on an MAGRNN neural network observer, and aims to solve the problems that a model prediction control method is insufficient in state estimation precision and worsened in control performance under the conditions of model mismatch, parameter offset and power grid voltage distortion. For an LCL type grid-connected inverter, firstly, a prediction model is established based on a state-space equation; secondly, real-time estimation parameters of an inductance parameter state observer are constructed; the method comprises the following steps: firstly, estimating an interference compensation item through a neuron self-adaptive adjustment mechanism, then introducing an MAGRNN neural network as a dynamic error compensator, using input and output of the observer as MAGRNN input, estimating the interference compensation item through the neuron self-adaptive adjustment mechanism, and finally, feeding back the interference item predicted by the MAGRNN to a model prediction controller for interference compensation. By utilizing the strong nonlinear fitting capability of the MAGRNN, the state estimation precision under complex working conditions such as parameter change and power grid disturbance is remarkably improved, and the control performance of the system is effectively improved.

A batch process two-dimensional model-free signal compensation control method with network packet loss and unmodeled dynamics belongs to the technical field of industrial process control, and comprises the following specific steps: step 1, describing a state-space equation of a batch process in a packet loss environment, and expanding the state-space equation into a linear model and unmodeled dynamics; 2, constructing a packet loss model in a network environment, and introducing a two-dimensional Smith predictor with packet loss compensation; 3, expanding the optimal Q function into a quadratic form, and deriving the optimal Q function to obtain an optimal value; 4, designing a control algorithm with signal compensation; 5, integrating packet loss compensation into a control algorithm, and solving an optimal control law; according to the method, the problem of network packet loss existing in the production process is solved, a two-dimensional Smith predictor is introduced, data packet loss compensation is effectively performed, unmodeled dynamics are solved and compensated through a method based on reinforcement learning, the influence of the unmodeled dynamics on the control performance is reduced, and the control effect is greatly improved.

The invention discloses a nonlinear adaptive control system design method and device based on a pseudo-inverse matrix, and relates to the field of adaptive control, and the method comprises the steps: S1, constructing a controlled object state-space equation comprising matching uncertainty and non-matching uncertainty; non-matching uncertainty is converted into matching uncertainty through pseudo-inverse transformation; s2, introducing the state vector into a regression vector to obtain an expansion regression vector, and obtaining an uncertainty function based on the expansion regression vector; s3, constructing a state observer, and obtaining a state observation value of the controlled object; s4, taking the difference between the state observation value and the state actual value as the excitation of the adaptive law, and updating the parameters of the uncertain function; s5, constructing a self-adaptive control law by taking the estimated value of the uncertain function as input; and S6, filtering an output signal of the adaptive control law through L1 to obtain a final control law. Through the expansion regression vector comprising the state vector, accurate compensation of the uncertain function is realized, and the self-adaptive efficiency is improved.