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

The application discloses a circuit system iterative learning control method for processing local Lipshitz nonlinearity under unknown state. The method aims at the actual problem that the circuit system current is unknown, and constructs an adaptive gain state observer based on a reference model. An iterative length selection index is constructed, so that the output of the system and the observer does not violate the given limit range. An iterative learning controller is constructed to control the circuit system with local Lipshitz nonlinearity. Compared with the traditional control method which needs an accurate mathematical model, the control algorithm is more novel and the application condition is simple. When the method is used for repeated task operation, the control precision is effectively improved, and the method has good engineering application value.

The invention discloses a stainless steel cold-rolled strip steel withdrawal and straightening elongation dynamic compensation strip shape control system and method. The system comprises an upper-layer coordinator, a lower-layer distributed actuator cluster and a communication bus. Predicting an elongation target curve of the full length of the strip steel based on the simplified dynamic model through an upper coordinator, and generating a reference trajectory and a constraint boundary of each roller section; self-adaptive MPC rolling optimization is executed once every 50 ms through a local controller of the lower roller section, and the roller gap pressure is adjusted by combining real-time tension and temperature data; a TSN communication bus is used for issuing the reference track to a lower-layer actuator, and meanwhile state deviation reported by the actuator is fed back to an upper-layer coordinator; and on the basis of an iterative learning control algorithm, global model parameters are updated every five seconds according to the state deviation. Accurate dynamic compensation of the strip steel elongation is achieved, the problems that a traditional control method lags in response and is low in compensation precision are solved, and the method has the advantages of improving control precision, enhancing system stability, improving dynamic adaptability and the like.

The invention discloses a filament current control method, a filament current control system and an X-ray machine. The filament current control method is used for controlling the filament current of a cathode filament in a bulb tube of the X-ray high-voltage generator, and comprises the following steps: when the bulb tube current of the bulb tube exceeds a preset condition, carrying out iterative learning on the corresponding filament current of each exposure parameter point through an iterative learning controller to obtain an exposure parameter point; and the corresponding filament current preset value IFRefSave of each exposure parameter point is updated after iteration, so that the filament current can be controlled by using the updated corresponding filament current preset value when a user uses each exposure parameter point for exposure again. By adopting the iterative learning method, the problem that the accuracy of short-time exposure is reduced due to the change of the corresponding relationship between the filament current and the bulb tube current caused by the aging reason of the long-time use of the bulb tube in the prior art can be effectively avoided.

The application discloses an iterative learning control evolution method for an automatic driving vehicle in a circulation scenario and relates to the field of automatic driving vehicle control. First, an offline controller is designed for the automatic driving vehicle in the circulation scenario, and reference states in the corresponding scenario are obtained. Then, based on the reference states and considering the influence factors that are difficult to traverse in the offline controller design, an online controller is designed. Finally, the online controller is continuously evolved through iterative learning until the learning process converges, and the optimal control effect is achieved. Based on the optimal control effect, the motion state of the automatic driving vehicle converges to the expected state in the circulation scenario, and the task in the corresponding scenario is executed. The application guarantees the effectiveness and real-time performance of the online controller, avoids the error accumulation in the iterative process, and improves the safety of the unmanned system.

The invention provides a vehicle system high-precision tracking iterative learning control method based on data. According to the method, the iterative operation process of a vehicle system is divided into test iteration and adjustment iteration; in test iteration, test input data are designed, test iteration is executed on the vehicle system, and corresponding test output data are collected; selecting a gain matrix of an iterative learning control algorithm by using the test output data, and directly embedding the test input data into the iterative learning control algorithm; in the adjustment iteration, based on the control input of the vehicle system under the current adjustment iteration, the collected input data, the selected gain matrix, the expected target output of the vehicle system and the output of the vehicle system under the current adjustment iteration, an iterative learning control algorithm is established, and the control input of the next adjustment iteration is updated; and controlling the vehicle to execute the next adjustment iteration. According to the invention, a high-precision tracking control scheme is provided for a nonlinear vehicle system with completely unknown model information and not satisfying quasi-regularization.

The application discloses a mechanical arm iterative learning control method based on a radial basis function network, which solves the limitations of traditional mechanical arm control methods in dealing with nonlinear characteristics and external disturbance problems in a complex dynamic environment. The method comprises the following steps: firstly, constructing a mechanical arm system dynamics model, and adopting a dynamic correction strategy to dynamically correct and optimize a reference trajectory; then, designing a radial basis function neural network to construct a nonlinear compensation term, and designing dynamic weight parameters and dynamic learning gains for optimizing the performance of a controller; finally, designing an iterative learning controller, and verifying the stability and error convergence of the control algorithm. Through the radial basis function network, the dynamic adjustment strategy and the iterative learning strategy, the control precision, adaptability and error convergence speed of the mechanical arm system are improved.

The application discloses a kind of direct current motor iterative learning control optimization methods for executing change task, it is related to direct current motor control field.The method is based on the closed loop feedback control system of direct current motor and parallelly joins iterative learning controller, based on promotion technique will direct current motor control system be converted into time series input-output matrix model.Under the norm optimization framework, optimal iterative learning control algorithm is designed, through the combination of batch-to-batch repeated learning and batch real-time feedback, so that the system basically realizes zero error tracking to expected output.Based on the optimal input sequence and error sequence obtained by repeatedly executing a task, the feedback and feedforward controller are integrated into a new learning-based feedback controller using least squares fitting method, and finally it is applied to the system executing the change trajectory task.This method transfers the historical learning experience to a new task without limiting its time length, and realizes the trajectory tracking of direct current motor change task without relearning.

The application discloses a horizontal hydraulic diaphragm pump and belongs to the technical field of diaphragm pumps. The horizontal hydraulic diaphragm pump comprises a power end, a hydraulic end, an auxiliary system and a control system. The hydraulic end comprises a hydraulic chamber adopting an asymmetric gradually changing cross section design, a gravity compensation type gradient rigidity composite diaphragm, a diaphragm chamber and an integrated intelligent exhaust device, the top of the hydraulic chamber is provided with a spherical crown gas collection concave cavity; the diaphragm is divided into a central flexible area, a transition buffer area and an edge rigid area along the radial direction, and a reinforcing framework is embedded in the lower semicircular area; the intelligent exhaust device comprises a bubble aggregation cavity, a gas-liquid separation microchannel group, a liquid level sensor and an exhaust electromagnetic valve. The auxiliary system comprises a double-cavity pre-pressing pulsation buffer, an air bag is arranged in a main buffer cavity, and a spring piston is arranged in an auxiliary buffer cavity. The control system adopts a pressure gradient self-adaptive reversing control and an iterative learning control algorithm to inhibit pressure pulsation, and solves the problems of asymmetric force bearing of the diaphragm, gas accumulation in the hydraulic chamber, large flow pulsation and reversing impact and the like under the horizontal arrangement.

This invention relates to a Kalman filter active damping iterative learning model predictive control method for grid-connected inverters. First, by using only measured signals of inverter-side current and grid-side voltage through a Kalman filter, the state variables of the LCL filter are estimated in real time, replacing physical sensors and reducing hardware costs. Second, based on the estimated capacitor voltage, the active damping current is calculated, and a composite current reference value for the inverter side is generated to actively suppress resonance. Then, an iterative learning control outer loop is introduced to compensate for periodic disturbances such as dead-zone effects, generating a compensation current using historical error data to improve current quality. Finally, finite set model predictive control is used to optimize the switching state, achieving high-precision tracking. This invention reduces the number of sensors, enhances anti-interference capability and robustness, and significantly reduces the total harmonic distortion of the output current. It is suitable for distributed renewable energy generation systems, ensuring the stable and efficient operation of grid-connected inverters.

The present application belongs to the technical field of photovoltaic module performance test, and relates to a photovoltaic module wind load spectrum simulation test system and method. The system comprises a closed fluid cavity provided with a fluid inlet and a fluid outlet; a fluid pressure control system comprising a pressure storage tank, a proportional valve, an on-off valve and a vacuum pump; the pressure storage tank is connected in sequence through the proportional valve, the on-off valve and the fluid inlet; the air inlet of the vacuum pump is connected with the fluid outlet; a spectrum analysis and control module comprising a data acquisition card and an iterative learning controller; the input end of the data acquisition card is connected with the output end of the pressure sensor, and the output end of the data acquisition card is connected with the input end of the iterative learning controller; and the output end of the iterative learning controller is connected with the proportional valve, the on-off valve and the vacuum pump respectively. The present application can reproduce any target wind pressure spectrum with high precision and fast convergence, and provides a high-fidelity and high-reliability dynamic loading means for wind fatigue resistance and accelerated life test of photovoltaic modules.

The application discloses an iterative learning control method based on master-slave architecture, and relates to the technical field of automatic control, which comprises the following steps: establishing a system energy optimal control problem model according to the dynamic model of a master system and a slave system, a relative reference path and system constraint conditions, obtaining system model parameters, a relative reference path signal, an uncertainty boundary and noise covariance information; adopting an alternating direction multiplier method to perform iterative optimization on the relative reference path signal, a feedback gain matrix and a master system tracking error boundary, synchronously updating slave system control input and a master system reference trajectory and judging convergence, and obtaining optimal slave system control input and the master system reference trajectory. The application introduces the alternating direction multiplier method to perform iterative optimization, decomposes a complex optimization problem into sub-problems which can be solved in parallel, improves calculation efficiency, and guarantees strict satisfaction of constraint conditions through projection transformation and a dual variable updating mechanism.

The invention discloses an unequal-length iterative learning control optimization method for a robot impedance control system, and relates to the field of robot control, and the method comprises the steps: building a high-dimensional linear state space model capable of describing nonlinear dynamic characteristics based on a Koopman operator theory; based on the model, an approximate linear repetitive process model of the robot impedance control system is further constructed, and an input and output matrix expression form of a time sequence is obtained through a lifting technology. For the unequal-length problem, a prediction compensation method is used, missing information caused by the unequal-length problem is predicted, and the track length is adjusted to the expected length. Under a norm optimization framework, the performance of future batches is considered, a predictive iterative learning control algorithm is designed, and finally the predictive iterative learning control algorithm is applied to an actual robot impedance control system. Control input obtained through the method can better adapt to an original nonlinear system, and high-precision tracking control over an expected trajectory is basically achieved.

The invention discloses a control method, system and terminal for a six-rotor full-drive unmanned aerial vehicle, and the method comprises the steps: obtaining the current speed information and attitude information according to an inertial measurement instrument, and generating position information based on extended Kalman filtering according to the speed information; acquiring an expected position, constructing an unmanned aerial vehicle power model according to the speed information and the expected position, and converting the expected position into an independent rotating speed of each rotor wing based on unmanned aerial vehicle parameters and the unmanned aerial vehicle power model according to the corrected position information corresponding to the speed information and the position information; obtaining resistance information and a preset filtering tracking error, and generating a correction error according to adaptive iterative learning control; and controlling the rotor wings of the unmanned aerial vehicle according to the independent rotating speeds of all the rotor wings of the unmanned aerial vehicle. According to the invention, through error introduction and rotation speed calculation, the control of the unmanned aerial vehicle can be accurately realized.

The invention discloses a multi-agent control method and system based on homomorphic encryption, and relates to the field of artificial intelligence, and the method comprises the steps: enabling a follower to receive an encrypted current input signal, an encrypted expected output signal and an encrypted current neighborhood output signal through iterative learning control, carrying out the decryption of the current input signal, and carrying out the decryption of the current neighborhood output signal; generating a first actual output signal, and calculating an error in combination with the expected output signal and the current neighborhood output signal; and when the maximum error of all followers is smaller than a preset threshold value, iteration is ended, otherwise, iteration is continued, a first actual output signal is encrypted and then sent to the neighborhood follower, an encrypted second actual output signal sent by the neighborhood follower is received, the encrypted signal is sent to the controller, and the controller sends back an encrypted update input signal. By implementing the method and the device, the problem that in the existing multi-agent consistency control, eavesdropping is easily caused during interaction through a public network or a cloud service is solved, and the security of information transmission and interaction is improved.

The invention relates to the technical field of distributed multi-unmanned aerial vehicle system cooperative control in the field of complex system robust control, and discloses a variable batch iterative learning-based switching topology multi-unmanned aerial vehicle system control method, which comprises the following steps of: firstly, establishing a discrete time nonlinear multi-unmanned aerial vehicle system model with a random test length; probabilistic modeling is carried out on the test length by adopting a Bernoulli random variable and recursive interval Gaussian distribution; then designing a distributed iterative learning control law, and effectively utilizing incomplete trajectory data to realize tracking error compensation by introducing a correction consistency error and an improved iterative average operator; and finally, through a switching topology mechanism for communication topology link quality, under the conditions of random test length, nonlinear dynamic, bounded disturbance and topology switching, index convergence of the tracking error of the multi-unmanned aerial vehicle system can be ensured, and the adaptability of the system in a variable batch task and the robustness of cooperative control are improved.

The application provides a kind of exoskeleton control method and system based on iterative learning and extended state observer, comprising: obtaining lower limb exoskeleton system parameters and motion state data, establishing nonlinear dynamics equation and unifying total disturbance term, obtaining multi-scale dynamics model by multi-time scale decomposition;Based on the model, an extended state observer is constructed to estimate the system state and total disturbance, and the disturbance is fed back to the control input;A game model between man and machine is constructed, parameters are identified online and an iterative learning control update law is designed to obtain an adaptive iterative learning control law;Design fast and slow dynamic controller, integrate related control information through hierarchical control architecture, and output multi-time scale model predictive control quantity.The application improves the coordination, comfort and energy efficiency ratio of human-computer interaction, and can provide stable and natural assistance experience in various complex environments, meeting the needs of rehabilitation training and daily assistance.

This invention provides a differential evolution iterative learning control method for non-uniform trajectory length of robotic fish. By introducing a differential evolution algorithm, the control gain in the non-uniform trajectory length iterative learning control method is optimized, resulting in faster convergence than traditional iterative learning control methods. Traditional iterative learning control methods cannot compensate for missing control information during the experiment; however, by introducing open-loop and closed-loop control, the originally missing control information is compensated by information from previous running cycles, enabling rapid tracking and convergence of the desired trajectory. In practical applications, this algorithm can be applied to situations requiring rapid trajectory tracking, reducing system runtime, improving engineering efficiency, avoiding energy consumption, and significantly saving engineering costs.

This invention relates to the field of industrial control technology, and more specifically, to a method for precise pressure adjustment in the injection molding holding process based on iterative learning control. The method includes: acquiring a reference holding pressure curve, an actual feedback pressure sequence, and injection molding holding control commands for the current holding stage; calculating thermal hysteresis characteristics characterizing changes in physical state; calculating a thermal drift evolution index characterizing the thermal drift rate; generating a nonlinear adaptive gain adjustment factor constrained within a safe range; and calculating and updating the injection molding holding control commands for the next holding stage. This invention more accurately reflects the comprehensive impact of changes in physical state, scientifically assesses the thermal drift rate and the difference between current and historical data, dynamically adapts to thermal drift to ensure control stability under different thermal conditions, effectively solves the problems of holding pressure overshoot, oscillation, and slow response, improves the dimensional consistency of injection molded products and the control accuracy of the holding process, and ensures production quality and efficiency.

The application relates to a high-order iterative learning control method and device based on an ant colony algorithm applied to a non-uniform interval of a single-link manipulator and computer equipment, the method comprising the following steps: optimizing a pre-constructed target single-link manipulator model by using an ant colony algorithm to obtain optimal control gain; designing a sampling error function of the target single-link manipulator system; designing an iterative learning control law of the target single-link manipulator model based on the sampling error function and the optimal control gain; controlling the target single-link manipulator model to perform high-order iterative learning, and updating a control signal of next iteration of the target single-link manipulator model by using the iterative learning control law until the sampling error function converges, and the iteration is stopped, so that the current control signal of the target single-link manipulator system is obtained. The application has the effects of faster convergence speed and higher control precision.

The application discloses a kind of based on the iterative learning model predictive control method of gas-driven soft robot of Koopman, including steps: system initialization and initial model construction;Model predictive control calculation based on predictive model;System response acquisition and tracking error calculation;Input based on iterative learning control and model correction;Model updating and iterative predictive control execution.The above-mentioned method, model predictive control is combined with iterative learning mechanism, and a unified prediction modeling framework is constructed by introducing Koopman operator, so that the control method can gradually compensate control error and maintain stable predictive control performance in the repeated operation process under the condition of system model uncertainty and parameter time-varying, suitable for the control application of pneumatic soft actuator and related flexible robot system.

The embodiment of the specification provides a kind of single connecting rod mechanical arm system iterative learning control method, device and medium, wherein, method includes: single connecting rod mechanical arm system is modeled, and discrete sampling period is set;Desired output trajectory of mechanical arm is discretely sampled, and desired output sequence is obtained;Actual control input signal is discretely processed to actual control input signal, and then actual output trajectory of mechanical arm is obtained, and actual output sequence is obtained according to discrete sampling period;Error sequence is obtained according to actual output sequence and desired output sequence, and error value is calculated, whether error value meets preset convergence condition is judged, if not, iterative learning control gain is calculated, and control input signal of next iteration is updated according to error sequence, iterative learning control gain and iterative learning control law, until error value meets preset convergence condition, stop iteration.

The invention discloses a high-order iterative learning control anti-disturbance optimization method and system based on a data mask, and the method comprises the steps: based on a high-order ILC controller, carrying out the tracking of a position tracking error signal sequence of a plurality of previous iterations, extracting a system trend term, obtaining the error signal sequence of the first iteration and the error signal sequence of the plurality of previous iterations, and obtaining the error signal sequence of the first iteration and the error signal sequence of the plurality of previous iterations during the first iteration; fitting error signal sequences of previous several iterations to obtain a reference fitting curve, calculating according to the reference fitting curve and the error signal sequence of the first iteration to obtain a residual sequence, identifying an abnormal disturbance section for the residual sequence, and performing weight adjustment on sampling points of the abnormal disturbance section based on a data mask and a suppression weight coefficient to obtain an abnormal disturbance section. And if it is detected that the residual error exceeds a preset threshold value, triggering an online compensator to obtain a torque compensation signal, and shielding the data of the abnormal disturbance section by using a data mask. According to the method, on the premise that an accurate model or a complex observation structure is not needed, accurate positioning and suppression of non-repeated disturbance are achieved with low calculation overhead.

This invention discloses a backstepping-based variable iterative learning control method for robotic arms, applicable to motor-driven robotic arm systems. This method converts the robotic arm system model into a third-order strict feedback form. By defining coordinate transformation error and introducing a command filter, an auxiliary system is constructed to compensate for the deviation between the filter and the virtual control law. A radial basis function neural network is used to approximate the unknown dynamics and disturbances of the system online. A backstepping controller is designed based on the compensation error, and a parameter learning law with variable iteration length is constructed to ensure system stability and convergence of the compensation error. By dynamically adjusting the iteration length during the control phase, this method can reduce computational resource consumption while maintaining high-precision tracking performance. When used for robotic arm control of repetitive tasks, it significantly improves the tracking accuracy and robustness of the system, demonstrating good engineering application value.