201 results about "Iterative learning control" patented technology

A human-guided vision-force fused impedance iterative learning control method for a robotic arm, comprising: analyzing a robot-environment interaction dynamics equation, solving a visual servo acceleration model, and making use of the equation to establish a human-robotic arm-environment interaction dynamics model in an image feature space; acquiring an image feature position and speed curve of a human-guided robot completing an assembly task, and using dynamic movement primitives for coding and generalization; and designing an impedance iterative learning controller which uses image feature tracking errors as control input, learning impedance characteristics when the human-guided robot performs a contact operation, identifying unknown contact dynamics under the interaction between the robot and the environment, and counteracting identified contact interference in the feature space, so as to implement a flexible assembly operation. The control method solves the problems in existing assembly operations that human-robotic arm-environment coupling nonlinear dynamics, unknown contact dynamics of intensive contact assembly tasks and poor generalization of assembly scenarios require relearning for different scenarios, etc.

The invention discloses an event-triggered iterative learning consistency control method for a multi-agent system under FDI attack, and belongs to the field of intelligent control. According to the technical scheme provided by the invention, the method comprises the following steps: establishing an autoregression model of an output sequence, and predicting model parameters by considering previous m iterations; after m iterations, adding an attack detection and compensation mechanism; based on topological structure information, a linear parameterized model between a consistency error epsilon k, j (t + 1) and a multi-lag input vector uk, j (t) is obtained through a state transition iteration dynamic linearization technology; designing a parameter estimator and an iterative learning control law; in order to reduce network communication resources, an event trigger mechanism model is established. According to the event-triggered iterative learning consistency control method for the multi-agent system under the FDI attack, a corresponding compensation scheme is added for the system after the attack, so that the influence caused by the attack is reduced, and the good control performance of the system is ensured.

The invention discloses a neural network adaptive iterative learning control method for a flexible joint robot, and the method comprises the steps: S1, building a constrained flexible joint robot, and initializing a system state, sampling time and control parameters; s2, compensating input saturation constraint; s3, calculating a system tracking error, and constructing an expected error trajectory; and S4, defining an error variable, and designing a neural network adaptive iterative learning controller. According to the method, the uncertainty of the flexible joint robot is estimated and compensated through the neural network, the error convergence precision is improved, by constructing the quadratic fraction type symmetric constraint Lyapunov function, it is guaranteed that the system output is always within the constraint range, the transient performance of the system is improved, and the reliability of the system is improved. According to the method, the flexible joint robot can safely and stably run in an iterative change task, when the number of iterations is increased, the tracking error is consistently converged to zero, and the method has the advantages of less required priori knowledge, no need of an accurate system model, high control precision and the like.

The invention discloses an iterative learning control optimization method for a direct current motor with non-repetitive disturbance, and relates to the technical field of direct current motor control, and the method comprises the steps: lifting a nominal model of a nonlinear system to a high-dimensional linear space through a deep Koopman operator, and obtaining the description of a high-dimensional state space model of the nonlinear system; converting the state space model into a linear repetition process model, and obtaining a linear matrix inequality condition of system convergence in a high-dimensional space by using a two-dimensional iterative learning control method; further, the intelligent agent is trained in a high-dimensional state space model iterative learning control system with model mismatch and a non-repetitive uncertainty nonlinear system, and finally the intelligent agent is applied to an actual system. The control law gain obtained by the method can better adapt to an original nonlinear system, a deep reinforcement learning algorithm is designed to compensate non-repetitive disturbance, and zero-error tracking of expected output is basically achieved.

The invention discloses a finite iteration error tracking learning control method for robot accurate trajectory tracking. The method comprises the steps of establishing a kinetic equation of a repeated operation robot system, determining a discrete dynamic model of the robot system, designing a finite iteration error tracking learning controller and carrying out simulation verification. According to the method, under the conditions that the initial position of the robot system is arbitrary and the expected trajectory changes, the number of iterations required for achieving arbitrary precision tracking of the given expected error trajectory by the output error of the robot system can be calculated by designing an error tracking learning controller and only utilizing error information obtained through first iteration. According to the method, the limitation that zero error convergence needs to depend on infinite iterations in a traditional iterative learning control method is effectively overcome; meanwhile, it is ensured that the robot system can still achieve the preset tracking precision with the rapid convergence capacity under the disturbance conditions that the initial position is arbitrary and the expected trajectory changes, and the practicability and the popularization value of iterative learning control in actual engineering are improved.

The invention relates to the technical field of precision automatic lathe machining, in particular to a precision automatic lathe workpiece machining method which comprises the steps that a double-spindle synchronous machining mode is adopted to achieve seamless connection of rough machining and finish machining, clamping errors are reduced, a servo motor drives a tool shaft to achieve nanoscale feeding compensation, and traditional mechanical transmission precision limitation is broken through. The integrated infrared temperature measurement module monitors the temperature of a cutting area in real time and dynamically adjusts the spraying angle and flow of cooling liquid based on machining parameters, machining thermal deformation is effectively controlled, and a dynamic weight self-adaptive interpolation model is established to balance machining efficiency and contour precision. Main shaft load, tool vibration and workpiece deformation data are collected in real time through a multi-sensor data fusion system, axial following errors are corrected through an iterative learning control algorithm, contour consistency is improved, surface defects are eliminated through a nanoscale vibration compensation mechanism, machining efficiency is improved, and machining precision is improved.

The invention discloses a five-phase permanent magnet motor multi-objective optimization method and a control system thereof, and belongs to the technical field of motor body design optimization and motor control. According to the multi-objective optimization method for the five-phase permanent magnet motor, the optimal optimization value of the motor parameters is obtained based on the genetic aggregation response surface and the multi-objective genetic algorithm, the design variables of the motor are comprehensively considered, and the optimization accuracy is improved; according to the control system of the five-phase permanent magnet fault-tolerant motor, iterative learning control serves as a current control technology and is used for recovering the performance of the five-phase permanent magnet motor under the fault condition, the advantages of iterative learning control and counter electromotive force control are combined, high-performance operation of the motor can be ensured under the fault condition, and the reliability of the five-phase permanent magnet fault-tolerant motor is improved. Ripple-free torque output is realized, and ohmic loss is minimized. The fault diagnosis module of the control system diagnoses short-circuit faults and open-circuit faults by using a time sequence neural network and a Kalman filter, and has real-time performance and strong adaptive ability.

The invention discloses a mechanical arm iterative learning control method based on a radial basis function network. The method solves the limitation that a traditional mechanical arm control method deals with nonlinear characteristics and external disturbance problems in a complex dynamic environment. The method provided by the invention comprises the following steps: firstly, constructing a mechanical arm system dynamic model, and dynamically correcting and optimizing a reference track by adopting a dynamic correction strategy; then designing a radial basis function neural network to construct a nonlinear compensation item, and designing a dynamic weight parameter and a dynamic learning gain for optimizing the performance of the controller; and finally, designing an iterative learning controller, and verifying the stability and error convergence of a control algorithm. Through the radial basis function network, the dynamic adjustment strategy and the iterative learning strategy, the control precision, the adaptive capacity and the error convergence speed of the mechanical arm system are improved.

The invention relates to an iterative learning control method for a non-repetitive time-varying system average operator, and the method comprises the steps: constructing a discrete dynamic model which allows system parameters to change in a non-repetitive manner along with the time and the number of iterations, and generating an expected trajectory and a tracking error of dynamic truncation; performing dynamic truncation processing on the randomly changed track length through random variables of Bernoulli distribution to generate an optimization correction error signal; constructing a variable track length average operator based on weighted average and correction error signals of historical control input, designing an iterative learning control law, and verifying the convergence of the iterative learning control law; and finally, the tracking precision and robust stability of the system under the variable trajectory length and variable initial state are verified through simulation. The method breaks through the limitation of traditional iterative learning control on hypotheses such as a fixed system model and a fixed test length, solves the problem of tracking failure of a non-repetitive time-varying system caused by parameter drift and trajectory abrupt change, does not need to depend on an accurate model or a large amount of data training, and has algorithm conciseness, real-time performance and interpretability.

The invention relates to the technical field of air door motor control, and discloses an air door motor control method and system based on current detection, and the method comprises the steps: carrying out the single-phase current signal collection of an air door motor system, and obtaining a current change signal in the opening and closing process of an air door; carrying out FFT (Fast Fourier Transform) and wavelet decomposition processing on the current change signal to obtain a current characteristic spectrum component and a time domain characteristic; processing the current characteristic frequency spectrum component and the time domain characteristic to obtain an air door load current characteristic; iterative learning control is executed, and air door rotating speed control parameters are generated; the PWM duty ratio is calculated, and air door motor driving pulses are generated; according to the air door motor control method, a complete air door motor control protection mechanism is established, abnormal conditions such as stalling can be detected and coped with in time, the safety of an air door motor system is improved, and the service life of the air door motor system is prolonged.

The invention discloses an online iterative learning control method and device based on bidirectional frequency binary search, and belongs to the technical field of tracking control, and the method comprises the steps: building a piezoelectric positioning system, and enabling the piezoelectric positioning system to have a certain low-frequency disturbance suppression capability while keeping stable through adjusting the parameters of a proportional-integral controller; constructing a control structure combining a YK parameterization method and iterative learning control; constructing a robust filter in iterative learning control by adopting a filter design method of a parallel cascade composite structure; a differential parameter constraint space is set for each filter based on a bidirectional frequency binary search method, and iterative updating of filter parameters is driven in a time domain by taking norm minimization of system errors as an optimization target, so that continuous optimization of online disturbance suppression performance is realized. According to the method, YK parameterization and an iterative learning control structure are combined, and the stability of the piezoelectric positioning system in the whole frequency domain can be ensured.

The invention discloses a multi-axis servo turntable system and a control method, and belongs to the technical field of mechanical control. The multi-axis servo rotary table system comprises a driving mechanism, a control unit and a rotary table body, and the driving mechanism comprises a plurality of servo motors and is used for driving the rotary table body to rotate and axially move; and the control unit controls each servo motor by using a fuzzy adaptive sliding mode iterative learning control algorithm. The sliding mode parameters are dynamically adjusted by introducing fuzzy logic, and the compensation of periodic disturbance by iterative learning is combined, so that the system buffeting is remarkably reduced, the dynamic response speed is improved, and the high-precision control of multi-axis collaboration is realized.

The application belongs to the technical field of industrial automation control, and provides an automation control method and system based on optimal iterative learning control and feedforward control. The automation control method comprises the following steps: constructing an automation control model by using an optimal iterative learning control algorithm and a model-based feedforward control algorithm; optimizing the automation control model; and implementing automation control based on the automation control model. The automation control method based on optimal iterative learning control and feedforward control solves the problems of performance decline of the optimal iterative learning control algorithm when the trajectory changes and possible degradation of the model-based feedforward control algorithm under actuator constraints, and can realize high-precision tracking and reliable control under repeated and non-repeated tasks, and is suitable for high-precision trajectory tracking and actuator constraint management of industrial motion systems.

The invention discloses a closed-loop feedback functional electrical stimulation control method and device, a medium and equipment, and relates to the technical field of computers. According to the invention, the current target motion angle of the healthy side limb of the patient and the actual motion angle of the affected side limb of the patient under the current functional electrical stimulation control signal are obtained, and the error between the target motion angle and the actual motion angle and the change rate of the error are determined; then, an adaptive weight fuzzy iterative learning control closed-loop feedback control algorithm is adopted to accurately adjust control parameters through learning of historical control experience, so that the affected-side limb of the patient can quickly approach a target movement track of the unaffected-side limb of the patient, and the iterative learning frequency of the iterative learning control algorithm is reduced; the limb movement reconstruction real-time performance is improved, the target trajectory tracking precision is improved, and the accomplishment degree of the limb movement on the affected side of the patient is improved.

The invention belongs to the technical field of intelligent control, and discloses a speed tracking self-adaptive iterative learning control method for an extrusion rod of an extruding machine, which comprises the following steps of: determining a dynamic model of the extrusion rod and a control target according to the physical property and the motion mode of the extrusion rod; based on an approximation theory, establishing a neural network model for an uncertain part in the extrusion rod motion model, and determining a speed tracking error system of the extrusion rod; designing virtual control input and actual control input based on a self-adaptive iterative learning control theory; based on the Lyapunov stability theorem, the stability of the controller is analyzed and designed. According to the speed tracking self-adaptive iterative learning control method for the extrusion rod of the extruding machine, limited time speed tracking of the extrusion rod can be effectively controlled, accurate motion control over the extrusion rod is achieved, and the size precision, the surface quality and the production efficiency of products are ensured.

The invention discloses a direct current motor iterative learning control optimization method for executing a changing task, and relates to the field of direct current motor control. According to the method, an iterative learning controller is added in parallel on the basis of a direct-current motor closed-loop feedback control system, and the direct-current motor control system is converted into an input and output matrix model of a time sequence based on a lifting technology. An optimal iterative learning control algorithm is designed under a norm optimization framework, and zero-error tracking of expected output is basically realized by the system through combination of inter-batch repeated learning and in-batch real-time feedback. Based on an optimal input sequence and an error sequence which are obtained by repeatedly executing a certain task, a feedback controller and a feedforward controller are integrated into a new feedback controller based on learning by utilizing a least square fitting method, and finally the new feedback controller is applied to a system for executing a change track task. According to the method, historical learning experience is transferred to a brand new task without limiting the time length, and trajectory tracking of the direct current motor change task can be realized without re-learning.

The invention provides a direct disturbance suppression method for a vibration isolation system based on driving force feedforward and linear active disturbance rejection control, and belongs to the technical field of active vibration isolation. The problems that existing active control methods such as feed-forward control and iterative learning control are limited by model dependence, nonlinear adaptation is poor, and aperiodic disturbance processing capacity is weak are solved. The method comprises the steps that a dynamic model of a vibration isolation system is established, the vibration isolation system comprises a vibration isolation platform and a motion platform, and the dynamic model is used for quantifying direct disturbance force caused by motion of the motion platform; a feedforward compensator is designed, and feedforward compensation force is generated in real time according to a motion instruction of the motion platform; introducing a linear active disturbance rejection controller; and superposing the feed-forward compensation force with a compensation force generated by the linear active disturbance rejection controller to obtain a total compensation force, and applying the total compensation force to the vibration isolation system to realize disturbance suppression. The method is mainly used in the field of precision manufacturing and measurement.

The invention discloses a linear friction welding machine vibration compensation control method based on iterative learning, and the method comprises the steps: collecting a vibration signal of an electro-hydraulic servo vibration system of a linear friction welding machine in a welding process, determining a vibration amplitude, frequency and vibration center deviation, and when the vibration amplitude or the center deviation exceeds a preset threshold value, starting the linear friction welding machine; the controller starts vibration compensation control; an electro-hydraulic servo vibration system model is established, the rigidity characteristic and the dynamic load change of the system are comprehensively considered, an iterative learning control law of a controller is designed, and control convergence conditions are analyzed to ensure the stability and the high efficiency of control; based on the output of the controller, the error trend of the vibration signal is predicted in real time in combination with historical iteration data, and a feed-forward control signal is generated through a feed-forward compensation mechanism to adjust the vibration signal error in advance. According to the method, the iterative learning is applied to the vibration compensation control of the linear friction welding machine, so that the precise compensation of the vibration signal is realized.

The invention belongs to the technical field of industrial automatic control, and provides an automatic control method and system based on optimal iterative learning control and feedforward control, and the automatic control method comprises the steps: constructing an automatic control model through employing an optimal iterative learning control algorithm and a model-based feedforward control algorithm; the automatic control model is optimized; and implementing automatic control based on the automatic control model. The automatic control method based on the optimal iterative learning control and the feed-forward control is utilized, so that the problems that the performance of the optimal iterative learning control algorithm is reduced during track change and the model-based feed-forward control algorithm is possibly degraded under the constraint of an actuator are solved; the method can realize high-precision tracking and reliable control under repeated and non-repeated tasks, and is suitable for an industrial motion system of high-precision trajectory tracking and executor constraint management.

The invention relates to the technical field of automatic control, in particular to a high-order iterative learning control method for a nonlinear non-repetitive system, which comprises the following steps of: 1, establishing a system model: aiming at the first iteration, establishing a discrete time multiple-input multiple-output system model with multi-source non-repetitive uncertainty; 2, defining and correcting a tracking error: introducing a random variable obeying Bernoulli distribution, and correcting the tracking error to process a track length of iterative change; 3, designing a high-order iterative learning control law: updating a current control signal by adopting control input and correction tracking errors based on a plurality of previous iteration periods; and 4, applying the high-order iterative learning control law to the controlled system, and updating the control input of the next iteration by using the corrected tracking error obtained by each iteration, so that the control output of the system tracks an expected trajectory under the meaning of mathematical expectation, and the expected value of the tracking error is finally converged to a bounded region.

The invention discloses a water turbine governor fault modeling and parameter optimization method based on iterative learning control. The method comprises the following steps: S1, system dynamics modeling; s2, iterative learning parameter updating, wherein a water turbine governor fault diagnosis method based on iterative learning control realizes progressive identification of fault features through periodically correcting model parameters; s3, fault feature extraction: calculating a residual signal of an actual output and a model predicted value, performing time-frequency analysis on the residual signal by adopting improved Morlet wavelet transform, and extracting an energy entropy feature and a time domain statistical feature; s4, fault diagnosis and dynamic optimization: based on the extracted fault features, fault detection and classification are realized through a three-level linkage decision mechanism, a dynamic adjustment strategy is introduced to carry out online optimization on a diagnosis rule base, and fault modeling and parameter optimization closed loop are completed; according to the method, the problem of modeling misalignment of a traditional method under a nonlinear working condition is effectively solved.

The invention relates to the technical field of medical equipment manufacturing. More specifically, the invention relates to an ultra-short wave therapeutic apparatus production consistency control method and system, and the method comprises the steps: building a virtual digital twin model according to the design parameters of an ultra-short wave therapeutic apparatus, and the model comprises a circuit model, an electromagnetic field model and a thermal model; various parameters of production equipment and products are collected in real time, and the collected parameters are compared with theoretical parameters in the digital twinborn model to determine the difference between the parameters; adjusting the parameters of the production equipment according to the difference so as to compensate the parameter drift of the component; and receiving radio frequency power data of the directional coupler, and calling an iterative learning control algorithm to optimize the driving voltage curve so as to realize dynamic adjustment of the radio frequency power. According to the scheme, the consistency and reliability of products are effectively improved, the production process is optimized, and quality tracing is achieved.

The invention relates to the technical field of relay monitoring and control, and discloses a method and system for monitoring a high-pressure gas relay and a storage medium. The method comprises the following steps: carrying out closed-loop control data acquisition on SF6 gas parameters of a high-pressure gas relay to obtain control loop operation data; calculating a control deviation and a residual vector for the data to obtain a control performance index; inputting the performance index into an isolation forest algorithm to identify abnormity, and obtaining a health state evaluation result; adaptive parameter adjustment is executed according to the evaluation result, PID parameters are optimized through iterative learning control, and a control parameter set is generated. According to the invention, closed-loop control system data acquisition is carried out on the SF6 gas parameters of the high-pressure gas relay, and the isolation forest algorithm is used for abnormal identification, so that the problem that a traditional fixed threshold monitoring method is easy to cause false alarm and missing alarm is solved.

The invention provides an electrical stimulation and lower limb exoskeleton device fusion control method and system, and the method comprises the steps: driving an exoskeleton to move through bottom admittance control based on the actual human-computer interaction torque and expected torque of a knee joint and reference trajectory data of the knee joint in a gait cycle, and triggering ankle joint electrical stimulation through a fixed stimulation parameter group; after the gait cycle is finished, torque errors are calculated, and periodic iteration updating is conducted on the knee joint reference trajectory data through an iterative learning control algorithm till convergence is conducted to obtain target knee joint reference trajectory data; and then determining a target moment of a gait phase based on the target knee joint reference trajectory data, and obtaining a corresponding target stimulation parameter group from the set stimulation parameter time mapping library to replace the fixed stimulation parameter group for subsequent stimulation. According to the method, through single-cycle and cross-cycle trajectory iteration, the adaptation defect of a traditional fixed trajectory is avoided, the stimulation parameters are synchronously optimized, and the dorsiflexion assisting effect is indirectly improved.

The invention relates to the technical field of water surface / underwater unmanned vehicle control, in particular to an unmanned vehicle neural network control method under online model identification. Comprising the following steps of: calculating and establishing a motion model of a water surface / underwater unmanned vehicle through a hydrodynamic theory, or establishing the motion model by adopting a neural network algorithm after real navigation data is acquired and processed in a still water state; a simulation system is established through the model, parameter simulation is controlled based on a model prediction control algorithm, and the horizontal direction is controlled; in the actual navigation process, the simulation control parameters are initially used for control, and online model identification is carried out on dynamic input data based on neural network algorithm model identification so as to correct model parameters; a modeling error is obtained and a feedback compensation control quantity is generated based on a neural network identifier compensation identifier, control parameters are optimized and then substituted into a neural network algorithm, so that the control effect is better, the control error is gradually reduced through iterative learning, and the optimal state is achieved.

The invention discloses a multi-agent variable expected trajectory iteration consistency tracking method and system. The method comprises the following steps: constructing a dynamic model and a network communication topological graph of a multi-agent system; determining a multi-agent tracking error, constructing a controller through a P-type open-loop learning algorithm of a high-order inner membrane, performing iterative control on a dynamic model of the multi-agent system, and outputting the multi-agent system with consistent tracking; verifying the multi-agent system with consistent tracking according to a compression mapping principle and norm properties; and based on the verified multi-agent system, tracking the multi-agent through a P-type iterative learning control algorithm based on a second-order internal model. According to the method, the corresponding iterative learning controller based on the high-order internal model can be designed for the intelligent agent on the basis of the observation error, and the consistency tracking precision of the variable expected trajectory is improved. The multi-agent variable expected trajectory iteration consistency tracking method and system can be widely applied to the technical field of intelligent control.

The invention discloses a data driving error tracking iterative learning control method for robot accurate trajectory tracking. The method comprises the steps of constructing a kinetic model of a repeated operation robot system, determining a discrete dynamic model of a robot, designing a data driving error tracking iterative learning controller, performing experimental verification and the like. According to the method, under the condition that parameters of a robot system are uncertain, the error tracking iterative learning controller is designed only by using input and output data generated by the robot system, and the robot tracking error is converged to an expected error trajectory within a limited number of iterations; wherein the designed error tracking strategy is used for reducing the requirement of the robot control system for the same initial state, so that the robot system can still achieve a better control effect in any initial state. Only input and output measurement data generated by the robot are used in the design and analysis process of the robot controller, the difficulty in the aspect of model decoupling is reduced, and practical engineering popularization is better facilitated.

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 invention discloses an FMCW laser radar real-time nonlinear correction device and method based on FPGA control, and the method comprises the steps: taking a DFB laser as a light source, driving the DFB laser by an initial driving current signal, generating an optical signal through a modulation end of the DFB laser, and inputting the optical signal into an FMCW delay heterodyne measurement module; the FMCW delay heterodyne measurement module converts the optical signal into an FMCW beat frequency signal and inputs the FMCW beat frequency signal into the FPGA module; the FPGA module generates a triangular wave driving signal from the FMCW beat frequency signal through a secant iterative learning control algorithm; the laser driving module comprises a current stabilization module, the current stabilization module converts a triangular wave driving signal from the FPGA module into a driving current signal, the driving current signal is input into a DFB laser, and the driving current signal is corrected through a laser radar nonlinear correction loop composed of the DFB laser, the FMCW delay heterodyne measurement module, the FPGA module and the current stabilization module. And the non-linear correction of the current signal is completed.

This invention discloses an iterative learning control method for a high-frequency fatigue machine based on a robust disturbance observer. The method includes: obtaining a mathematical model of the high-frequency fatigue machine through system identification using a sinusoidal frequency sweep method; designing a robust disturbance observer based on the mathematical model of the high-frequency fatigue machine; estimating system disturbances using the robust disturbance observer based on the control input and output of the iterative controller, thereby achieving cancellation with the actual system disturbances; ultimately improving the disturbance rejection capability and tracking performance of the high-frequency fatigue machine. This invention not only fully utilizes iterative learning control to achieve high tracking accuracy but also utilizes a robust disturbance observer to improve system control performance.