156 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 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 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 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 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 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.

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.

The invention discloses a two-dimensional adaptive iterative learning control method of a magnetic control shape memory alloy actuator, and belongs to the technical field of tracking control of the magnetic control shape memory alloy actuator. According to the method, on-line data of a magnetic control shape memory alloy actuator is directly utilized for data driving modeling, a mathematical model of a system does not need to be known in advance, the system is described as a tight-format dynamic linearization model, and a self-adaptive controller is designed. And then, in order to effectively utilize information on an iteration axis, combining an adaptive controller on a time axis with classical P-type iteration learning control, and designing a two-dimensional adaptive iteration learning controller. According to the controller, the control law is continuously updated on the iteration axis, the defect of open-loop control of the one-dimensional iteration learning controller on the time axis is overcome, and disturbance on the time axis is effectively dealt with.

The present invention discloses a multi-objective optimization method and its control system for a five-phase permanent magnet motor, belonging to the technical fields of motor body design optimization and motor control technology. The multi-objective optimization method for the five-phase permanent magnet motor of the present invention obtains the optimal optimization values of motor parameters based on genetic aggregation response surface and multi-objective genetic algorithm, comprehensively considers motor design variables and improves optimization accuracy; a control system for a five-phase permanent magnet fault-tolerant motor proposed by the present invention uses iterative learning control as a current control technology to restore the performance of the five-phase permanent magnet motor under fault conditions, and combines the advantages of iterative learning control and back electromotive force control, can ensure the high-performance operation of the motor under fault conditions, achieve ripple-free torque output, and minimize ohmic losses; the fault diagnosis module of the control system uses a time series neural network and a Kalman filter to diagnose short-circuit faults and open-circuit faults, and has real-time performance and strong adaptability.

The present application relates to the field of liquid level control technology, and in particular to a liquid level control system and method. The present application analyzes the received liquid level data based on a predictive iterative learning control model to obtain a liquid level control result, generates a liquid level control instruction based on the liquid level control result, and sends the liquid level control instruction to the actuator so that the actuator controls the liquid level of the liquid in the water tank according to the received liquid level control instruction, wherein the predictive iterative learning control model is a predictive model established along the iteration axis based on a linear data model, and the linear data model is a nonlinear mechanism model established by analyzing the dynamic material balance relationship of the water tank. In this way, the present application is aimed at a networked liquid level control system with a repeated operation feature, and by combining predictive control and iterative learning control methods, it reduces the adverse effects of the data attenuation problem existing in the transmission of the networked system on the controller performance, and improves the accuracy of the liquid level control.

The application discloses a multi-axis servo turntable system and a control method, and belongs to the technical field of mechanical control. The multi-axis servo turntable system comprises a driving mechanism, a control unit and a turntable body. The driving mechanism comprises a plurality of servo motors and is used for driving the turntable body to rotate and axially move. The control unit controls each servo motor by using a fuzzy self-adaptive sliding mode iterative learning control algorithm. By introducing fuzzy logic to dynamically adjust the sliding mode parameters and combining the compensation of periodic disturbance by iterative learning, the system chattering is significantly reduced, the dynamic response speed is improved, and high-precision control of multi-axis cooperation is realized.

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 present invention discloses a method for adaptive iterative learning collaborative control of a movable beam and an extrusion rod of an extruder, which relates to the technical field of multi-agent adaptive iterative learning collaborative control and includes the following steps: constructing a dynamic model of the movable beam and the extrusion rod; determining a control target; determining a position error tracking system according to the control target, and designing a controller and a parameter update law based on adaptive iterative learning control theory; and performing theoretical simulation verification. The present invention adopts the above-mentioned method for adaptive iterative learning collaborative control of a movable beam and an extrusion rod of an extruder, regards the movable beam and the extrusion rod of the extruder as intelligent bodies, and combines adaptive iterative learning control theory to effectively realize the collaborative control of the movable beam and the extrusion rod; the method has self-regulating control capabilities, can produce products with higher precision requirements under the influence of nonlinear factors, improve production efficiency and reduce production costs, and is easy to expand and adapt to production needs of different scales.

The invention relates to the technical field of permanent magnet motor network control, and discloses an iterative learning control method, system, equipment and medium for a permanent magnet motor network, and the method comprises the steps: a permanent magnet motor end obtains an actual control input signal of a permanent magnet motor and a corresponding actual output sequence, carries out the homomorphic encryption, and transmits the signals to a controller end; the controller end calculates the encrypted actual control input signal, the encrypted actual output sequence and the encrypted expected output sequence through an iterative learning law to obtain an encrypted calculation control input signal, and sends the encrypted calculation control input signal to the permanent magnet motor end; and the permanent magnet motor end decrypts the encrypted calculation control input signal and inputs the obtained calculation control input signal as an updated actual control input signal into the permanent magnet motor to obtain an updated actual output sequence. According to the method, high-precision control can be realized in a scene of random data loss, and comprehensive data privacy protection is realized while the calculation efficiency is reduced, so that efficient real-time control on the permanent magnet motor is ensured.

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 present invention discloses a fault-tolerant iterative learning control method for a robotic arm system based on a balance factor. Based on the derivative relationship between the parameters of the robotic arm system, the system is reconstructed into a second-order system, a backstepping error and a virtual controller are constructed. At the same time, to avoid the imperfect reset of the system state at the beginning of each iteration, a parameter update law based on the balance factor is constructed, and an iterative learning controller is designed to control the robotic arm system and compensate for actuator faults, parameter uncertainties and disturbances. Compared with traditional control methods that require an accurate mathematical model, the control algorithm proposed in the present invention is more novel and has simple application conditions. When this method is used for repetitive task operations, it effectively improves the control accuracy and has good engineering application value.