145 results about "Steady state error" patented technology

The invention relates to the technical field of chip test engineering control, in particular to an active temperature control method and system for a chip aging tester, and the method comprises the steps: analyzing a test process according to test task parameters, and evaluating the expected thermal load change rate of a test chip, so as to dynamically generate a chip target junction temperature control curve; based on the chip target junction temperature control curve, the actual chip junction temperature and the current actual furnace temperature, the theoretical furnace temperature is obtained through a junction temperature closed-loop control algorithm, the furnace temperature compensation amount is calculated through a feedforward compensation algorithm in combination with the test state parameters, and the ideal furnace temperature is obtained according to the theoretical furnace temperature and the furnace temperature compensation amount; the actual temperature control quantity is obtained according to the basic temperature adjusting quantity and the temperature control compensation quantity, a preset chip target junction temperature control curve can be tracked more accurately, transient and steady state errors caused by test program switching and environment fluctuation can be more effectively restrained, and control over the chip junction temperature with higher precision and faster response is achieved.

The invention discloses a self-adaptive control method and system for a condenser for a nuclear power station, relates to the field of condenser control, and aims to predict a future load change trend in a prospective manner by analyzing historical data of a unit power instruction and steam flow, and calculate feed-forward circulating water flow based on a prediction result. On the basis, a feedback control loop based on actually measured pressure is reserved. More importantly, a self-adaptive bias correction mechanism is introduced, and continuous learning and correction are carried out on output of feedback control, so that steady-state errors of the system are eliminated, and uncertainty of the model is compensated. Finally, feed-forward, feedback and self-adaptive correction components are intelligently fused to form a final control instruction which can quickly respond to load changes and can accurately maintain pressure stability, and the problems of severe pressure fluctuation and energy waste under the dynamic working condition are effectively solved.

The invention discloses a pouring machine flow control system based on multi-parameter feedback and PID dynamic adjustment, and relates to the technical field of automatic control of mold pouring, the system comprises a tilting parameter module, a liquid level dynamic modeling module, a flow adaptive control module and an execution mechanism module; the tilting parameter module collects the angular velocity and angle data of a casting ladle in real time, the liquid level dynamic modeling module calculates the liquid level height according to the angular velocity, the angle data and the geometrical shape of the casting ladle, and the flow self-adaptive control module dynamically calculates the current flow value. And the preset flow target and the real-time flow difference value are input into a PID controller to generate a tilting adjustment signal to drive an execution mechanism module to adjust the flow of the casting machine. The liquid level height and the flow are calculated through mechanical motion parameters, the problem that the sensor is in direct contact with liquid in the high-temperature and high-pressure environment and fails is solved, and stable operation in the severe environment is guaranteed; and adaptive PID control and a multi-parameter fusion model are used for cooperative work, so that the steady-state error is reduced, and the response speed is increased.

The invention provides an electric vehicle wireless charging control system optimization method based on a three-stage particle swarm algorithm, which considers transient performance and steady-state performance in the charging process of an electric vehicle, and divides the charging process into three stages: a starting stage, a tracking stage and a stabilizing stage. The first stage is a starting stage, and overshoot and voltage rising speed are mainly considered; the second stage is a tracking stage, and voltage tracking time and switching loss are mainly considered; and the third stage is a stable stage, and mainly considers steady-state errors and switching loss. Through a particle swarm optimization algorithm, parameters and duration of an optimization controller in each stage are optimized, and voltage fluctuation at a stage switching node is minimized, so that the system can achieve optimal performance in each stage. Compared with a traditional overall control scheme, the performance of the wireless charging system of the electric vehicle can be remarkably improved, the system loss is effectively reduced, and the purposes of high efficiency, economical efficiency and safety are achieved.

The present invention belongs to the technical field of parameter identification for permanent-magnet synchronous motors. Disclosed are a parameter identification method and apparatus for a permanent-magnet synchronous motor, and a storage medium and a device. The method comprises: using a variable step-size feedback neural network algorithm to solve a voltage equation and a d-q axis current discrete state equation, which take cross-coupled inductance into consideration, of a permanent-magnet synchronous motor in a dq coordinate system, so as to obtain intermediate parameters generated in the process of constructing the d-q axis current discrete state equation; and performing calculation on the basis of the intermediate parameters, so as to obtain the stator resistance, the d-axis inductance, the q-axis inductance, the cross-coupled inductance and the permanent-magnet flux linkage of the permanent-magnet synchronous motor, and thereby completing parameter identification. The present invention takes cross-coupled inductance into consideration and uses the variable step-size feedback neural network algorithm, such that the computational load is extremely small, the degree of complexity is reduced, rapid and effective identification can be realized, and errors can be reduced; in different convergence time periods, the algorithm adjusts the step size on the basis of the magnitude of steady-state errors, such that identification results approach stable values more quickly, thereby improving the speed and stability of convergence.

The invention discloses an integral adaptive admittance control method and system, a computing control device and a storage medium. The method comprises the steps that 1, a target admittance model is established and initialized; step 2, establishing an adaptive admittance controller according to an adaptive control strategy based on environmental information identification, and deriving a reference trajectory corresponding to the target admittance model; step 3, establishing an integral admittance controller, performing steady-state error optimization processing on the reference trajectory, and generating an optimized reference trajectory; and step 4, generating a robot motion instruction according to the optimized reference trajectory. According to the method, firstly, an integral controller and online environment parameter identification are deeply fused, steady-state errors are eliminated through integral terms, dynamic environment changes are compensated through parameter identification, and stability collaborative optimization is carried out. And secondly, a reference trajectory is dynamically adjusted by introducing a force error integration link, so that the stability contradiction between integration control and an adaptive law is solved, and the unification of high-frequency response and lightweight control is realized.

The invention discloses a cooperative robot joint adaptive control system and method based on multi-sensor fusion, and relates to the technical field of robot joint control. According to the invention, through an improved extended Kalman filtering and deep neural network compensation mechanism integrated by the multi-source data fusion unit, sensor noise is effectively suppressed, and multi-source data conflicts are solved; the sensor reliability evaluation unit is based on a dynamic weight distribution mechanism of Bayesian reasoning, and can reduce the fusion weight of the sensor in real time when the sensor is detected to be abnormal, and improve the contribution degree of reliable sensor data. The adaptive control unit is combined with the self-learning optimization unit to form a closed-loop optimization architecture, so that the performance limitation caused by fixed traditional PID parameters is thoroughly solved; through adaptive weighted fusion of multi-source sensor data, the joint state estimation precision is improved; and a deep neural network residual error compensation mechanism is utilized to reduce a torque control steady-state error.

The invention relates to the technical field of electronic component reliability engineering and environmental engineering, and discloses an electronic component environmental adaptability reliability prediction and life evaluation system and method, and the system comprises an environmental parameter collection module, a reliability prediction module, a life evaluation module, and a data storage and interaction module. According to the invention, a multi-dimensional sensor array including temperature, humidity, vibration, electromagnetic radiation and salt mist concentration is adopted by an environmental parameter acquisition module, multi-physical field stress parameters are synchronously acquired, and common environmental interference factors of electronic components are covered; a Kalman filtering algorithm is built in the data preprocessing unit to suppress environmental noise, the noise suppression ratio is increased by 40%, then abnormal values are eliminated through the 3 sigma criterion, and the data integrity reaches 99% or above; besides, the stress simulation sub-module realizes single / multi-stress coupling simulation through a temperature control cabin, a vibration table and the like, and ensures that the simulation stress steady-state error is less than or equal to 1% in combination with PID feedback control, so that extreme environment data of a laboratory can be supplemented, and the problem of insufficient field data is solved.

According to the flow control system and method for the filter element flow resistance performance test provided by the invention, the self-adaptive PID control closed loop taking the real-time flow resistance coefficient as the core feedback quantity is constructed, and the high-precision sensing network and the intelligent decision algorithm are integrated; the defect that traditional manual or simple PID control is difficult to adapt to filter element characteristic change and environmental interference is successfully overcome; specifically, the system dynamically collects flow, pressure difference and temperature signals and calculates the flow resistance coefficient of a filter element in real time, PID control parameters (proportion, integration and differential terms) are adjusted in a self-adaptive mode according to the flow resistance coefficient, meanwhile, the reference value of a sensor is periodically calibrated so as to eliminate drift errors, and an electric control valve is driven to rapidly and accurately respond; according to the method, the dynamic stability and static precision of flow control are remarkably improved, multi-source interference such as pipeline pressure pulsation and fluid temperature change is effectively compensated, the steady-state error of the test flow is greatly reduced, and finally the efficient and high-precision automatic test of the flow resistance performance of the filter element is achieved.

The invention relates to the technical field of electric power simulation, and discloses a hardware-in-the-loop simulation-oriented new energy equipment model verification method and system, and the method comprises the steps: constructing a new energy equipment basic model based on a physical equation, carrying out the compensation of a dynamic working condition error through combining with an LSTM neural network, and generating a hybrid parameterized model; fitting voltage and current data through a particle swarm optimization algorithm, and dynamically adjusting impedance parameters to complete model calibration; a time domain step response test, frequency domain characteristic analysis and a voltage sag fault experiment are executed on a hardware-in-loop platform, and the performance of the model is verified in a multi-dimensional mode; and generating a standardized verification report containing a steady-state error and a simulation step length index based on the deviation between the dynamic response curve and the measured data. According to the method, a mechanism model and a data driving technology are fused, the problems that dynamic characteristic adaptation is insufficient and parameter calibration depends on experience in traditional verification are solved, and the simulation precision and verification efficiency of the model under complex working conditions are improved.

The invention discloses a permanent magnet synchronous motor model-free sliding mode control method based on a novel reaching law, belongs to the field of power electronics, and aims at solving the problems that traditional sliding mode control is large in buffeting and high in steady-state error and depends on an accurate model. According to the method, based on a model-free theory, a permanent magnet synchronous motor rotating speed ring super-local model is constructed, then a novel reaching law containing a dynamic parameter delta = e-x beta is designed, and the contradiction between buffeting suppression and quick response is solved; meanwhile, designing a novel sliding-mode observer, and estimating an unknown part g of the system to realize disturbance feed-forward compensation; and finally, a q-axis current reference value control law is obtained through derivation in combination with a sliding mode surface and a mechanical motion equation. The method can weaken the buffeting of the sliding mode, improves the anti-interference performance and the steady-state precision of the system, guarantees the finite time convergence of the state, and is suitable for the control of the permanent magnet synchronous motor in the fields of aerospace and new energy automobiles.

The invention discloses a zero-sum game-based two-degree-of-freedom helicopter safety controller design method, which relates to the technical field of two-degree-of-freedom helicopter control, and comprises the steps of constructing a system equation, introducing an attack signal and a preset performance function, decomposing into two stages of subsystems, and designing a virtual controller, an adaptive law and a self-triggering mechanism. And solving a Hamiltonian-Jacobian-Exophone equation by using a zero-sum game theory, and finally verifying the stability of the system through a Lyapunov function. According to the invention, it can be ensured that the tracking error converges to the steady-state error interval within the predetermined time, and the transient and steady-state performance of the system is significantly improved; and meanwhile, a Nussbaum function and a zero-sum game strategy are introduced to improve the attack resistance, the communication burden and actuator abrasion are reduced through a self-triggering mechanism, and the sesame effect is avoided.

The invention discloses a mechanical arm self-adaptive variable impedance constant force control system which comprises an inverse kinematics unit, a fractional order active disturbance rejection controller unit, a mechanical arm and a self-adaptive variable impedance controller unit, and the input of the system is a tail end expected position and expected contact force. According to the method, the force overshoot is remarkably reduced, the steady-state error approaches zero, and the force tracking precision and robustness are improved.

The present invention provides a dynamic current-sharing technology based on series capacitor charge balancing and a three-phase series capacitor Buck converter device thereof, belonging to the field of power electronics. This control method ensures that the three-phase series capacitor Buck converter maintains good steady-state accuracy while rapidly responding to sudden changes in load current. This control method simplifies and solves the problem of ensuring that all state variables reach new steady-state values ​​simultaneously at the end of a transient process when implementing charge balancing control in a three-phase series capacitor Buck converter. This control method offers the advantages of a constant switching frequency, independence from converter circuit parameters, zero steady-state error, and rapid load transient response.

The invention relates to the field of power systems, in particular to a power system load frequency adaptive regulation and control system and method, and the system comprises a state observation module which collects a real-time frequency deviation signal to obtain a state vector representing the dynamic state of the system; the strategy network module is used for receiving the vector and outputting a control action of a generator power regulating variable; the system interaction module applies the control action to a controlled system or a simulation model of the controlled system to obtain a next state vector and an instant reward signal; the simulator network module predicts the frequency deviation at the next moment according to the current state vector and the control action; the strategy optimization module is used for updating strategy network parameters by using zero-order optimization based on prediction output; and the hybrid control switching module is used for comparing the absolute value of the frequency deviation with a preset threshold value and selectively outputting a control action or a PI controller signal. The method solves the problems that the prior art depends on an accurate model, reinforcement learning is divergent due to Critic network high variance training, gradient is abnormal, and pure DRL is large in steady-state error and poor in compatibility.

The invention provides a peristaltic pump control method for a pneumatic mechanical gripper, which relates to the field of intelligent control, and comprises the following steps: constructing a pneumatic conversion module, a peristaltic pump PID control module, a driving execution module, a bending sensing module and an error feedback processing module; the bending angle difference of the mechanical gripper is calculated and subjected to pneumatic conversion to obtain a bending error; the position type PID outputs a control signal, and Kp, Ki and Kd are set on line through a self-adaptive migration optimization algorithm; adjusting and updating individuals by utilizing a direction perception consensus index, a virtual adsorption point set and an adsorption step length; generating a time offset factor correction time sequence based on the historical response memory function; and the corrected signal drives a motor, and precise control over the mechanical gripper is achieved. According to the method, overshoot and steady-state errors are remarkably reduced, response time is shortened, oscillation is suppressed, manual parameter adjustment is reduced, and robustness is improved.

The invention provides a four-point supporting three-point leveling mechanism and a leveling method for a precision measuring table, and belongs to the technical field of precision measuring table leveling. Four-point supporting is achieved through the two piezoelectric ceramic stacking micro-displacement mechanisms and the two air springs, stability is improved, communication of the two air springs is equivalent to single-point supporting, three-point leveling is achieved by controlling the two piezoelectric ceramic stacking micro-displacement mechanisms, and control precision is improved; a piezoelectric ceramic stack is adopted to push a top block, a leveling top ball and a wedge block to realize micro-displacement adjustment, and high-precision control and quick response are completed. The four-point supporting three-point leveling mechanism of the precision measuring table has good dynamic stability, the steady-state error reaches 10-5 degrees, friction lag and assembly stress caused by surface contact are avoided through single-point contact of the leveling top ball and the wedge-shaped block, and precision automatic leveling in the mandrel measuring process can be achieved.

The invention discloses a self-adaptive unmanned aerial vehicle control method for an improved neural network. The method comprises the following steps: constructing a PID controller SSA-BP-PID of a BP neural network; optimizing a PID controller SSA-BP-PID based on a sparrow search algorithm SSA, updating a population position and assigning a value to a weight value and a threshold value of the SSA-BP-PID, so that the SSA-BP-PID outputs an updated PID parameter and a control law; calculating output attitude data of the aircraft according to the control law in combination with an aircraft kinetic model; and S3, setting the number of iterations, constructing a fitness function as a minimization target in combination with the overshoot, the adjustment time and the steady-state error, and iteratively calculating the steps S2-S3 to obtain output attitude data for controlling the flight of the unmanned aerial vehicle. The method is used for optimizing PID controller parameters, compared with a traditional PID control method, the method has remarkable advantages in the aspects of dynamic response speed, steady-state precision, anti-interference capacity and the like, and the control performance of the four-rotor unmanned aerial vehicle is improved.

The invention discloses an automatic control method and system for a multifunctional machining clamp, and particularly relates to the technical field of automatic control, and the method comprises the steps: firstly obtaining and determining the characteristic data of a workpiece, and carrying out the preprocessing of the data; the clamping time, the number of workpieces and the weight of the workpieces are analyzed, a coincidence coefficient is calculated through a weighted summation formula, and a proper clamp is selected according to the numerical value of the coefficient. Meanwhile, the magnitude of the clamping force is determined based on the characteristics of the workpiece and the selected clamp. In the machining process, the clamping force between the clamp and the workpiece is monitored in real time, and when the clamping force changes, timely adjustment is conducted through a PID algorithm. Besides, in combination with steady-state error response time and system noise frequency of the workpiece, an adjustment coefficient is calculated by using a logistic regression algorithm, and PID parameters are dynamically optimized according to the coefficient so as to adapt to different machining conditions. And finally, comparing the CAD data of the workpieces with the workpiece characteristic data, and screening out defective workpieces.

The invention discloses a control method of an asymmetric CLLC resonant converter adopting mixed control of track control and PI control. PI control is adopted during steady-state operation of the asymmetric CLLC resonant converter, when input voltage fluctuation is detected, trajectory control is introduced, the frequency of the converter entering the steady state again after the input voltage fluctuation is calculated through the geometrical relationship of a state plane, and steady-state errors are eliminated through a PI controller. The method has the advantages that the combination of the state track control and the linear controller can reduce the voltage change of the output side and shorten the adjustment time when the input voltage fluctuates, so that the problem of severe fluctuation of the output voltage caused by the sudden change of the input voltage when the resonant converter operates is solved, the dynamic performance of the converter is remarkably improved, and the reliability of the converter is improved. Finally, the problems existing in the process of designing the linear controller are solved, the problem of voltage abrupt change of the input side is solved, and the requirements for rapidity and stability are met.

The invention discloses a parallel formation low-bandwidth pulse switching control method and device and a storage medium, and the method comprises the following steps: predicting an initial duty ratio according to the voltage relation of Buck-Boost topology; generating a feedforward prediction duty ratio through a feedforward compensation mechanism; dynamically selecting a loop switching mode according to a current error absolute value or a current change rate, and determining a current loop duty ratio; the loop switching mode comprises a current open-loop control mode and a current closed-loop control mode; when the loop switching mode is a current closed-loop control mode, a PI parameter is dynamically adjusted according to a current error value; and when the current average change rate and the current error meet the compensation condition, the current loop is controlled to enter steady-state control. According to the invention, the current switching speed and precision are obviously improved, the step response overshoot is greatly reduced, and the steady-state error minimization is ensured.

This invention discloses a design method for a submarine vertical plane motion controller based on a closed-loop gain shaping algorithm. Based on this algorithm, the submarine depth controller and pitch controller designed according to the state-space equations of the submarine dynamics model can quickly and accurately achieve variable depth and constant depth maneuvers, optimizing the response time and steady-state error of the submarine's vertical plane motion controller, thus improving the submarine's underwater performance and safety. By constructing the depth and pitch nonlinear functions of the submarine's vertical plane motion controller, and combining these functions with the control laws of the bow and stern rudder actuators obtained from the vertical plane motion controller, the linear error feedback of the submarine's vertical plane motion controller is improved, enhancing the controller's robustness and energy-saving effect, reducing the impact of external interference, and decreasing the energy consumption of the servo motors.

The invention relates to a permanent magnet synchronous motor model prediction control method under an improved discrete domain voltage equation, in particular to a motor control method, and particularly relates to a control method for a permanent magnet synchronous motor under a high-speed and low-carrier-ratio working condition. According to a traditional motor voltage equation, in a single control period of a discrete domain, a dq axis is in a static state, and the position is kept as a rotor position at the beginning of the period. Considering actual rotation of a dq axis in the process, voltage components of current, flux linkage and a basic vector are reduced to the dq axis synchronously rotating along with a rotor from the original static dq axis through coordinate transformation, and considering contradiction between continuous change and control discretization of each quantity in a single period, equivalent values of the quantities are taken in one period, and the quantity is calculated to be the equivalent value of the dq axis. And obtaining a new motor voltage equation as a predictive control model. According to the method, the dq-axis voltage equation of the motor is precise, the effect is particularly remarkable under the working condition of high speed and low carrier wave ratio, and the steady-state error of the d-axis current can be eliminated.

The present invention relates to a mobile robot control method and system based on fault prediction. The system includes: a multimodal inverse kinematics solver that dynamically selects a solution mode based on the Jacobian matrix condition number. The system selects, from smallest to largest condition number, the transposed matrix method, damped least squares method, and constrained quadratic programming to calculate joint angle adjustments, adapting to adverse working conditions such as singular configurations; an adaptive integral controller that independently adjusts the position / attitude loop gain via a decoupling gain matrix, dynamically suppresses vibration and reduces steady-state error by combining error and fault index; and a fault prediction and reconstruction subsystem that calculates the joint fault index in real time. This index integrates torque deviation, temperature, harmonic distortion, vibration, and wear data, and implements a graded response. The system significantly improves singularity stability, control decoupling, and fault response speed, reducing the risk of secondary faults.

The application provides a control system for mechanical arm contact force zero steady-state error tracking, comprising: a collection device, a control device and an execution device; wherein the control device adopts a mobility controller fused with a reinforcement learning algorithm; the reinforcement learning algorithm obtains an expected position adjustment amount based on the actual contact force between the mechanical arm and the object, the actual position of the mechanical arm end and the force error between the actual contact force and the preset expected contact force collected by the collection device; and adjusts the preset expected position using the expected position adjustment amount; and the mobility controller outputs the updated position of the mechanical arm end according to the input force error after the expected position is adjusted. The application fuses the reinforcement learning into the mechanical arm contact force tracking system, not only ensures the rationality and accuracy of the expected position, but also realizes that the steady-state error of the system is 0, and ensures that the contact force between the mechanical arm and the object is equal to the set expected contact force.

The application provides a shield machine oil cylinder control method and device and a storage medium, and relates to the field of shield machines. The method comprises the following steps: acquiring first oil cylinder parameters of all oil cylinders of the shield machine in an initial state; determining a first total thrust and a first action point; acquiring second oil cylinder parameters in a region to be assembled after the oil cylinders enter a synchronous advancing state; determining a second thrust target value of a second type of oil cylinder in a non-assembled region; determining an oil cylinder mode switching step number; controlling the first type of oil cylinder and the second type of oil cylinder to perform multiple first thrust adjustments until the number of executions reaches the oil cylinder mode switching step number, then reducing the thrust of each first type of oil cylinder to 0, and controlling the first type of oil cylinder to retract, so as to realize segment installation. The application solves the problem that the prior art ignores the influence of sudden changes in the thrust of the oil cylinders in the assembled region on the resultant thrust and the target point, and only focuses on the steady-state error after the target thrust distribution is completed, thereby reducing the control efficiency of the oil cylinders.

This invention discloses a spacecraft configuration-maintaining attitude-orbit coupling cooperative iterative learning control method, belonging to the field of space technology. The implementation method of this invention is as follows: a six-degree-of-freedom dynamic model is established based on the Lie group SE(3) framework, fully considering the attitude-orbit coupling problem caused by thruster installation errors in actual engineering, improving configuration-maintaining accuracy, and avoiding the unwinding problem caused by traditional six-degree-of-freedom dual quaternion representation methods. Based on this, a cooperative iterative learning control method is designed to achieve, in the case of an unknown precise perturbation model, offset the influence of perturbation on configuration maintenance through iterative learning, taking into account both adjustment time and steady-state error, achieving a short adjustment time while maintaining a small steady-state error. That is, high-precision six-degree-of-freedom configuration maintenance of the spacecraft is achieved through attitude-orbit coupling cooperative iterative learning control. This invention can shorten the adjustment time, reduce steady-state error, and improve the speed and accuracy of spacecraft configuration maintenance without relying on a precise perturbation model.

The invention particularly relates to a direct force carrier landing control method based on predefined time active disturbance rejection control, and belongs to the technical field of aviation. Aiming at the problems of high landing accident rate and large wake flow disturbance influence of a shipboard aircraft, uncertain convergence of a traditional control method and the like, the method comprises the following steps: firstly, establishing a six-degree-of-freedom nonlinear model, an aerodynamic force and an aerodynamic moment model of the shipboard aircraft, and fusing the wake flow disturbance influence; constructing a torque control channel and a direct force control channel, and converting the torque control channel and the direct force control channel into a control-oriented model; and then designing PTADRC-based attitude angle, angular rate and track angle control laws, and realizing total disturbance estimation and compensation through PTESO. Simulation verification shows that the method has no overshoot and no steady-state error tracking instruction in the predefined time, is high in anti-interference capability, can guarantee the stability and accurate control of the landing attitude of the shipboard aircraft, and remarkably reduces the landing risk.

This invention provides a temperature balancing control method for high-temperature superconducting magnets using circulating cold helium gas. This method, which belongs to the field of superconducting magnet temperature control, includes: calculating the difference between the target temperature and the actual measured temperature to obtain the steady-state error for each magnet group; constructing a mathematical model of the cold helium cooling system based on singular perturbation theory; clarifying control objectives to improve the overall performance and stability of the cold helium cooling system; and constructing a singular perturbation control model based on the variable domain Smith. This invention can improve the response speed and accuracy of the cold helium cooling system.

The invention discloses a photoelectric encoder signal processing method, system and device and a readable storage medium, and the method comprises the steps: determining an error angle according to a current angle signal and a historical estimated angle after the current angle signal of a motor rotor collected by a photoelectric encoder is obtained, and inputting the error angle into a PI controller, the target angular velocity of the motor rotor is determined, and then the target angular velocity is integrated to determine a target angle signal. It can be seen that the error angle existing in the current angle signal is determined according to the historical estimated angle, rapid dynamic response is carried out through the PI controller, the steady-state error in the current angle signal is eliminated, then the precision of the finally obtained target angle signal is ensured, and the problem that when the motor controller controls the motor based on the target angle signal, the stability of the target angle signal is influenced is solved. The phase delay of the motor relative to the power grid further ensures the stable operation of the motor and the whole power grid system.