138 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 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 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 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.

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 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 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 an event triggering ILC of a mixed triggering strategy for a nonlinear system of which the iteration length is dynamically changed due to external interference or internal parameter fluctuation. The event triggering mechanism comprises a relative threshold strategy and a zero error tracking strategy, and the tracking performance of the system is ensured while the communication and calculation frequency is reduced. A Bernoulli distribution function is introduced to describe the probability of the random variable iteration length, and a corrected input updating strategy is provided. Traditional iterative learning control can effectively improve tracking precision of repeated tasks by extracting rules from historical execution data and optimizing control input by using task repetition characteristics, but depends on a global updating mechanism of a fixed period, and may cause redundant calculation or communication burden in non-uniform sampling or resource limited scenes. By designing an iterative updating strategy triggered by an event, the system can selectively utilize historical data under a dynamic triggering condition, and only executes learning and optimization when necessary, so that the resource consumption is reduced while the control precision is guaranteed.

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 present invention discloses a secure iterative learning control method based on homomorphic encryption. The method comprises: establishing a secure iterative learning control system; constructing a secure iterative learning controller; inputting an initial unencrypted input signal into a controlled object for control, and the controlled object outputting an initial unencrypted output signal; collecting data from a sensor and sequentially quantizing and encrypting the signal to obtain an initial encrypted output signal, and inputting the initial encrypted output signal into a cloud platform; determining whether the tracking error between the unencrypted output signal and a preset reference output signal reaches a preset indicator, repeating the iterative execution until the preset indicator is reached, completing the control of the controlled object, and terminating the control. The present invention performs signal transmission and control signal update in an encrypted manner, thereby ensuring the information security of the system while completing the established control task.

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.