339 results about "Iterative learning control" patented technology

The invention discloses a multistage-control-based batching weighing control method, and belongs to the technical field of weighing. A batching weighing process is divided into three stages, and different control modes are adopted. The method specifically comprises the following process of rapidly batching any material at the same constant maximum speed when the actual blanking amount is less than or equal to a first-stage weight value in the first stage which is a rapid feeding stage; performing control by using a conventional PID (proportion integration differentiation) controller in the second stage which is an accurate weighing stage; and performing control by using an iterative learning control method, calculating a given closing advance control amount, and correspondingly regulating the feeding speed in the third stage which is a drop prediction stage. According to the method, the problem of conflict between the material batching weighing speed and accuracy is well solved; in addition, the method is independent of changes in the material level of a hopper, random changes in the ratio of materials and the like, and has the advantages of high material batching weighing speed, high accuracy and the like.

By applying time-frequency analysis to a given standard iterative learning control or ILC an adaptive filter for the learned feed-forward loop is designed. This time varying filter varies according to the momentary frequency content of the error signal and allows to discriminate between areas of deterministic and stochastic error. Its application results in selective application of ILC to those intervals where error signals of high level are concentrated and allows application of a single ILC acquisition to different setpoint trajectories. The adaptive filter finds particular use in lithographic scanning systems where it is used for varying scan length.

The invention discloses an iterative learning trajectory tracking control and robust optimization method for a two-dimensional motion mobile robot. The method includes the steps that firstly, a kinetic equation of a two-dimensional motion mobile robot discrete non-linear motion system model is established; a discrete non-linear state space expression is established; a P type open-closed loop iterative learning controller based on the iterative learning control technology is established; then the robust convergence of the established discrete non-linear control system is theoretically analyzed; then parameter item splitting is conducted on control gains of the P type controller, meanwhile, a quadratic performance index function based on controller parameters is designed, and the purpose is to optimize the control parameters; finally, monotone convergence characteristics of output errors and parameter selection conditions generated when a control algorithm acts on a controlled system are analyzed and optimized, and the two-dimensional motion mobile robot can rapidly track an expected motion trajectory at high precision. The method has the advantages that the robust optimization iterative learning controller is suitable for tracking control in an ideal state and suitable for trajectory tracking tasks under the condition that interference exists outside. A designed iterative algorithm is simple and efficient, introduction of a large number of additional parameter variables is not needed, and engineering realization is easy.

The invention discloses an SCARA robot trajectory tracking control method based on prediction indirect iterative learning. Aiming at the problem that users are not allowed to compensate torque signals output by a servo drive of an alternating-current motor under most conditions in actual engineering, the SCARA robot trajectory tracking control method based on prediction indirect iterative learning is proposed. Firstly, a double-closed loop feedback controller directly acted on a robot body is designed, and includes a P type position closed loop and a PI type speed closed loop; and then, a prediction iterative learning controller (A-ILC) with a feedforward effect is designed, and a control effect at the sampling time t in next operation is adjusted by using error output information at the sampling time t+delta in previous operation batches. Compared with a proportional differential iterative learning controller (PD-ILC), the A-ILC is faster in iterative convergence speed and higher in tracking precision; and compared with an A-ILC without the feedforward effect, the A-ILC with the feedforward effect can eliminate external disturbance more quickly and effectively.

The invention discloses an advanced iterative learning control method for accurate target tracking, providing an iterative learning control method which utilizes tracking error information at advanced time as correction quantity to realize the accurate tracking of an uncertain time-delay system for the given desired output trajectory. The method not only has robustness for the uncertainty of a system model, but also can compensate the uncertain time delay of the controlled system. The method not only has simple structure and is easy to realize, but also has high accuracy and fast convergence rate, and has very high actual value.

The invention relates to an auto-disturbance rejection controller-based iterative learning contour error control method for a networked multi-axis motion control system. According to the method, firstly, the uncertainty of the system caused by time-varying delay is dynamically processed as one part of the total disturbance of the system, and the total disturbance of the system is expanded into a new variable. In this way, an augmented model of a networked single-axis servo control system is established. Secondly, an expanded state observer is designed to estimate the state of the augmented system, and then a linear self-disturbance rejection controller based on the expanded state observer is adopted to realize the tracking control of a single-axis trajectory. Thirdly, the contour error model of the system at the current moment is calculated. According to an obtained contour error, a contour error compensation controller based on an iterative learning control algorithm is designed. In this way, the high-precision tracking control on the contour of the system is achieved. The method realizes the good tracking control performance for the single-axis trajectory, and the good anti-disturbance capability for the model uncertainty of the system. The high-precision tracking control performance for the contour of the system is also achieved.

Embodiments of the present invention are directed to compensating for force ripple of an apparatus driven by a force produced by a linear motor. In one embodiment, a method of compensating for force ripple comprises generating force commands for a trajectory starting at a plurality of starting positions of the apparatus driven by the linear motor to produce different trajectory motions based on the same trajectory at the plurality of starting positions, the force commands each including peaks of large acceleration / deceleration and valleys of low force levels; calculating an average of the force commands during large acceleration / deceleration generated based on trajectory motions for the plurality of starting positions; calculating a variation ratio of the force command for each trajectory motion to the calculated average of the force commands; and compensating for force ripple in the apparatus based on the calculated variation ratio to control the force applied by the linear motor to the apparatus.

The invention discloses a model prediction tracking control method in batch chemical engineering. According to the invention, the method introduces an improved state space model to the model prediction control and iterative learning control method so as to increase the control properties in batch manufacturing. Different from traditional state space models, the method herein, in the state model structure in the proposed method, introduces the dynamic combination between a process state variable and output tracking errors, and uses the improved process model. By using the improved technical solution, the method can obtain controllers which have more freedom to regulate control properties, and can achieve better controlling effects.

The invention aims at an intermittent process with uncertainty, and proposes a constrained 2D tracking control method for the uncertainty intermittent process. The constrained 2D tracking control method comprises the steps of: firstly, designing an iterative learning control law for a given system dynamic model; secondly, converting the original system dynamic model into a 2D-FM closed-loop systemmodel expressed in the form of a predictive value according to a 2D system theory and the designed iterative learning control law through introducing state errors and output errors; and finally, giving a sufficient condition expressed in the form of a linear matrix inequality (LMI) for ensuring robust asymptotic stability of a closed-loop system and an expression form of the optimal control law according to a designed infinite time domain performance index and a Lyapunov stability theory. According to the constrained 2D tracking control method, numerical values of tracking errors under the control of the constrained 2D tracking control method are smaller, and the convergence is faster; more importantly, the control input does not drastically fluctuate and only require slight adjustment, thereby being conducive to resource conservation and reducing troubles caused by frequent operations.

A virtual soft tissue control system that provides enhanced motion control to a prosthetic simulator machine. The control system advantageously adds a “virtual soft tissue” control scheme to a conventional control system, such as a digital proportional integral derivative (PID) controller, to algorithmically model the soft tissue constraints that would be encountered by the prosthesis within the human body, and account for these forces in driving the simulator. In another aspect, a prosthetic simulator comprises a prosthetic drive mechanism; a feedback control system that drives the prosthetic drive mechanism; and an iterative learning control system that determines an error from a previous iteration of motion of the drive mechanism and uses the error to determine a drive signal for a subsequent iteration of motion. In certain embodiments, the prosthetic simulator uses both a soft tissue model and an iterative learning control system.

The invention relates to a comprehensive forecast and iterative learning control method based on the 2D theory, and belongs to the technical field of industrial production automatic control. According to the control method, as for most batch processing, manufacturing and producing processes, a two-dimensional system model including time and batch is built, a mode based on 2D system response description is adopted to obtain an output forecast value, and control correction is further calculated by means of the comprehensive forecast and iterative learning control method. The method is particularly suitable for the situation that the controlled process has random noise, is suitable for product quality tracking control in the intermittent producing and processing process, is ingenious in concept, simple, practical and capable of being widely used in high-precision track curve tracking control in the intermittent production process of an industrial production line.

The invention discloses an improved iterative learning control method and an improved iterative learning control system for a permanent magnet linear synchronous motor (PMLSM). The control method comprises the following steps of: calculating the control voltage of a stator of the PMLSM by adopting a time axis and iteration axis superposed iterative control law algorithm; introducing an initial control variable u0 on the time axis of the iterative control law algorithm, and designing an adaptive factor alpha; and adding a feedforward control variable to the (k+1)-iterated control voltage of the PMLSM following an iterative learning law. The control system comprises an embedded controller, a power driving module connected with the stator of the PMLSM, and a rotor displacement sensor arranged on the PMLSM. The oscillation of an initial iteration track is avoided, and an iterative convergence speed is increased; and control accuracy is improved to be 0.55 percent.

An anti-interference iterative learning control method for space manipulator system for capturing non-cooperative targets includes first analyzing and classifying multi-source interference of the space manipulator system in the case of capturing non-cooperative targets in orbit, and establishing a system coupling dynamic model containing the multi-source interference; designing a disturbance observer to estimate and compensate for external disturbance torque and disturbance torque caused by the target non-cooperation, adopting robust H8 control to suppress the random noise of an internal sensor of the space manipulator system with a bounded norm and evaluated errors of the disturbance observer, and using iterative learning control to perform iterative correction on trajectory tracking errors of the system; finally combining the control based on the disturbance observer, the robust H8 control and the iterative learning control, and forming a complete anti-interference iterative learningcontroller, and solving a gain matrix of the disturbance observer and the anti-interference iterative learning controller. The anti-interference iterative learning control method has the advantages of high anti-interference capability and high trajectory tracking control precision, and can be used for in-orbit high-precision operation control of the space manipulator system for capturing the non-cooperative targets.

The invention discloses a high-voltage parallel hybrid active power filter device and an iterative learning control method with a forgetting factor. The device is composed of a passive power filter, an active power filter and a coupling transformer. A steplength-variable fuzzy self-adapting harmonic wave detection method is adopted, so as to coordinate the requirements of steady-state performance and convergence rate of the system, and the detected electric network harmonic current is used as the system control target. An iterative learning control algorithm with the forgetting factor is adopted, and an improved Ziegler-Nichols method is utilized to realize the optimization of control parameters and improve the system control performance. The high-voltage parallel hybrid active power filter device has certain advantages in response time and control accuracy, can satisfy the requirements of large-capacity hybrid active filtering system, not only can well control the harmonic wave in the electric network, but also can compensate large-capacity reactive power, and is reliable in systems operation.

An iterative learning control method for tracking consistency of a fractional-order multi-agent system comprises the following steps: a, transforming the control problem of coordinated tracking of fractional-order multi-agent systems with different orders into a stability control problem of a tracking error system in a fixed time interval; b, designing a distributed P-type iterative learning controller with initial state learning capability; and c, solving an initial state learning matrix and an iterative learning gain matrix to be determined in the iterative learning controller. The inventionsolves the problem of coordinated tracking in the case where the initial state offset and the model unknown exist simultaneously in the fractional-order multi-agent systems with different orders by using the iterative learning control method, and the proposed iterative learning controller is not only simple in design and solving, but also can resist the offset of the initial state, and can ensurethe consistency of fractional-order multi-agent systems with different orders during the whole moving process after certain number of iterations, thereby having high practicability.

An inverted pendulum self-adaptive iterative learning inversion control method is characterized in that: aiming at an inverted pendulum system containing unknown input saturation, a self-adaptive iterative learning inversion controller is designed through utilization of a neural network and an inversion control method in combination with self-adaptive iterative learning control; the construction of an integral lyapunov function solves the control problem caused by derivation of a unknown gain function; based on the median theorem, a hyperbolic tangent function is adopted to approximate an input saturation term; then, a radial basis function neural network is adopted to approximate and compensate uncertain unknown items of a system, and two combined self-adaptive laws are adopted to updatethe weight of the neural network and the bound of estimation errors. Under the condition that the system has input saturation, the invention provides the control method which can compensate the unknown uncertainty of the system, solve the control problem caused by derivation of the unknown gain function and realize two-norm convergence of a tracking error of the system to be near zero within limited iteration times.

By applying time-frequency analysis to a given standard iterative learning control or ILC an adaptive filter for the learned feed-forward loop is designed. This time varying filter varies according to the momentary frequency content of the error signal and allows to discriminate between areas of deterministic and stochastic error. Its application results in selective application of ILC to those intervals where error signals of high level are concentrated and allows application of a single ILC acquisition to different setpoint trajectories. The adaptive filter finds particular use in lithographic scanning systems where it is used for varying scan length.

The invention discloses a mechanical arm fractional order iterative learning control method and system with an initial state learning function. The mechanical arm fractional order iterative learning control method with the initial state learning function comprises the steps that firstly, a kinetic model of a mechanical arm system is established, and an expected movement track of the mechanical arm system is preset; secondly, the initial state of the state quantity of the mechanical arm system and system input of the mechanical arm system are initialized, and the actual movement track of the mechanical arm system is worked out according to the kinetic model of the mechanical arm system; thirdly, whether the tracking error between the actual movement track and the expected movement track is equal to zero or not is judged through calculation, if yes, the actual movement track overlaps with the expected movement track, and the process is ended, and if not, the next step is executed; and fourthly, the initial state of the state quantity of the mechanical arm system is corrected according to the initial state of the tracking error and a set initial state learning gain, system input of the mechanical arm system is corrected according to the tracking error, a set input learning gain, and a fractional order, the actual movement track of the mechanical arm system is worked out accordingly, and then the third step is executed.

The invention belongs to the field of the advanced control of industrial process, and relates to a multi-stage intermittent process 2D linear quadratic tracking fault-tolerant control method. The method comprises the following steps: step one, establishing a switching system model taking a controlled object as the basis of a state space model and provided with fault 2D for different stages in theintermittent process; step two, by considering a 2D switching system model containing a free terminal state and for non-least realizing different stages, designing an intermittent process linear quadratic 2D iterative learning controller for an infinite time domain of the controlled object for a normal system, namely, an optimal controller; and step three, finding out a system stability conditionand designing a switching signal for the novel 2D switching system model. Through the method disclosed by the invention, a correspondingly simple controller capable of flexibly adjusting in real timeis designed according to different stages and an executor fault, the controller has a certain robustness, thereby improving the control quality; the design is simple, the computation burden is small,the optimal control performance of the system is guaranteed, the system operation time is shortened, and the efficient production is realized.

A method for controlling cascade-stage iteration learning cross coupling contour errors of a triaxial numerical control system, comprises the procedures of confirming practical reference input quantity of the triaxial numerical control system, a following error formula of an X axle, a following error formula of a Y axle and a following error formula of Z axle, deducing the convergence conditions for cascade-stage iteration learning control (ILC), obtaining the necessary convergence condition as a formula (2) of the cascade-stage ILC according to a formula (1), and realizing the contour error control of the triaxial numerical control system by getting a iteration learning law and a contour error controller meeting the conditions of the formula (2). The method for controlling the cascade-stage iteration learning cross coupling contour errors of the triaxial numerical control system has excellent dynamic compensation and smooth contour errors and being capable of improving the control performance.

The invention discloses a method for automatically compensating a numerical control machining size error based on a fractional order. The method comprises the following steps of performing machining, and measuring the size of a machined workpiece to obtain actual machining size data; comparing expected size data with the actual size data to solve a size error, and analyzing the error to identify a system order; calculating the amount of error compensation to be applied before the machining of the next workpiece according to the system order and a size error generated during the machining of the previous workpiece by adopting an iterative learning control law; and correcting a numerical control machining program for the workpiece by using the amount of error compensation before the machining of the next workpiece, and performing numerical control machining on the next workpiece by adopting the corrected numerical control machining program. The size error is automatically compensated by programming and developing numerical control machining size error compensation processing software based on the steps. The size error of the machined workpiece is integrally reduced, the machining accuracy is improved, the qualified rate of the workpiece is remarkably increased, and the remarkable error compensation effect is achieved.

The invention discloses a method for suppressing the torque ripple of a permanent magnet synchronous motor (PMSM) based on robust iterative learning control and relates to the technical field of PMSM rotational speed control. In view of the torque ripple of the PMSM and the parameter perturbation and the external load disturbance in a servo system, a robust iterative learning controller is designed by combining adaptive sliding mode control with iterative learning control. The iterative learning control learns an unknown periodic function based on a system state in the system to suppress the system periodic pulsating torque. The sliding mode variable structure control enables a system trajectory to operate along a sliding mode surface to improve the anti-disturbance performance of the system. In view of a sliding mode control buffeting problem, an adaptive law is designed to estimate system disturbance and compensate an estimated value to the controller, thereby weakening sliding-mode buffeting while guaranteeing system robustness. The method, in combination with the advantages of the iterative learning control and the sliding mode control, suppresses the torque ripple of the PMSM while improving the system robustness.

The invention provides a trajectory updating integrated prediction ILC algorithm under restricted conditions, and aims at problems in point-to-point tracking control in an intermittent process. Under the condition that input and output of a controlled system are restricted (including restriction of the direct input size, restriction of input change in time and batch orientations and restriction of the output size), trajectory tracking and error convergence tracking are realized, the advantage that a trajectory update algorithm takes both time orientation performance and utilization of the freedom degree of non-key points into consideration is reserved, the convergence speed is higher and the tracking error is lower compared with a traditional algorithm, input and output restricted conditions are taken into consideration, and the tracking error is lower, the anti-interference capability is higher and the application range is wider compared with a restriction-free trajectory update algorithm.

The present invention belongs to the field of automation technology advanced control, and especially relates to a batch process 2D constraint fault-tolerance control method through infinite time domain optimization. An iteration learning control law is designed for a constraint fault control system model with interference, state errors and output errors are introduced, a Roesser model is employedto convert a dynamic model of an original system to a closed-loop system model expressed by a prediction mode, and the designed iteration learning control law is converted to a determined constraint update law; and according to the designed infinite optimization performance index and a 2D system Lyapunov stability theory, giving an update law real-time online design which can ensure the asymptotically stable closed loop system robustness in a linear matrix inequality (LMI) mode. The problem is solved that the control performance cannot be improved with the batch progressive increasing and theproblem of initial value indetermination batch process is solved, and finally, energy saving and consumption reduction are achieved, the cost is reduced, and the generation probability of accidents being harmful for personal safety.

The invention relates to a high-frequency angular vibration rotary table sliding-mode control method based on iterative learning and belongs to the technical field of measurement and control. The method comprises the steps of conducting mathematical modeling on a permanent magnet synchronous motor, designing a sliding-mode variable structure control law and an iterative learning control law, and conducting sliding-mode control based on the iterative learning control law. Due to the fact that the sliding-mode control arrival process is replaced with the iterative learning control law learning process, the frequency response bandwidth of an angular vibration rotary table is increased, high-frequency inertial navigation device testing precision is improved, high-band periodic signal tracking precision of the angular vibration rotary table is improved, the influence of periodic disturbance and stochastic disturbance on a system is effectively restrained, the responding speed of the system is high, the tracking error is small, and robustness is higher.

The invention discloses a speed fluctuation suppression method for a permanent magnet synchronous motor. The method comprises the following steps: obtaining a target rotating speed and a feedback rotating speed of the permanent magnet synchronous motor, and calculating a fluctuation rotating speed of the permanent magnet synchronous motor according to the target rotating speed and the feedback rotating speed; carrying out iterative learning control on the fluctuation rotating speed to obtain a compensation speed, and superposing the compensation speed to a given rotating speed in a previous control period of the permanent magnet synchronous motor to obtain the give rotating speed in the current control period; and controlling the permanent magnet synchronous motor to suppress rotating speed fluctuation of the permanent magnet synchronous motor according to the given rotating speed of the current control period. According to the control method, the compensation speed is obtained through iterative learning control on the fluctuation speed to correct the given speed of a speed ring, so that effective control on the speed fluctuation when the permanent magnet synchronous motor runs is achieved. The invention further discloses a control device for the permanent magnet synchronous motor, and a compressor control system with the control device.

The invention discloses a robust iterative learning control strategy for a five-degree-of-freedom upper limb exoskeleton system. The control strategy employs a multivariable iterative learning item toestimate time-varying parameters in an upper limb exoskeleton model, and does not need to build an accurate mathematic model; besides, in order to improve tracking precision and obtain high robustness, sliding mode control is introduced into an iterative learning scheme; and a sliding mode controller is used for processing non-periodic lumped disturbance, and a self-adaptive law is designed in the control strategy and used for compensating uncertainty in the controller, so that a buffeting phenomenon caused by a switching item is weakened, tracking errors between human and machines are reduced, and tracking errors of all joints can be converged to be close to zero within limited iteration times. According to the control strategy, the tracking control problem of the five-degree-of-freedomupper limb exoskeleton system robot under unknown model parameters and aperiodic disturbance is solved, and the control strategy is high in algorithm robustness, small in control buffeting and good intracking performance.

Provided is a segmentation filtering iterative learning control method of a motor servo system. The method comprises steps of firstly determining learning law convergence frequency domain scope is 0-f<c0> when a zero-phase filter, i.e., Q(z)=1, is not introduced; then performing reference trajectory track of the primary iteration-free loop so as to obtain an error signal e0(t), applying the EMD algorithm to decompose the e0(t) into limited number of intrinsic mode functions, and obtaining time sequence characteristics of the error signal e0(t) via the Hilbert conversion; then using frequency f<c0> as the segmentation reference frequency; segmenting the error signal e0(t), and distinguishing time segments where instant frequency of the error signal e0(t) is higher than the f<c0> and lower than the f<c0>; designing cut-off frequency of a zero phase according to the Hilbert time sequence characteristics of the e0(t); in the time segment where the instant frequency of the error signal is higher than the f<c0>, f<c>=f<c0>+1 / 2(f<max>-f<c0>) is adopted as the cut-off frequency of the filter, wherein fmax represents the highest frequency in this segment; in other time segments, f<c0> is adopted as the cut-off frequency of the filter; and at last, applying the designed segmentation zero phase filter to the following iterative learning control processes.

The invention provides a multi-point mooring cooperative adaptive iterative learning control method. The method includes the following steps that: system initialization is carried out, design parameters and process variable parameters are obtained; the design parameters and the process variable parameters are substituted into a multi-point mooring system dynamic model; whether position tracking error satisfies a requirement is judged; and if the position tracking error does not satisfy the requirement, adaptive iterative learning open / closed-loop control laws are learned until the position tracking error satisfies the requirement, and iterative circulation is continuously carried out, so that a multi-point mooring system moves according to mooring ideal trajectories. According to the multi-point mooring cooperative adaptive iterative learning control method provided by the invention, the multi-point mooring cooperative control system adopts open / closed-loop iterative learning control,and therefore, the system has load feedforward and real-time feedback performance, the dynamic rapidity and static precision of the system can be improved, the stability of the system is high, the precise positioning of the multi-point mooring system can be realized, and the implementation complexity of the algorithm is low.

In view of the large urban traffic volume, large city scale, complex structure and other characteristics of China, the present invention provides a road network traffic signal iterative learning control method based on MFD. The road network traffic signal iterative learning control method based on MFD includes the steps: S1, 1.1) acquiring traffic data of a subarea MFD; 1.2) performing subarea MFDfitting; and 1.3) determining the ideal occupancy of roads, based on MFD; and S2, 2.1) performing an opened and closed loop iterative learning control strategy; 2.2) establishing a state space equation; and 2.3) optimizing signal time assignment of each intersection. The road network traffic signal iterative learning control method based on MFD enables the overall structure of the road network toachieve relative balance, can improve the outflow vehicles of the subarea so as to improve the traffic volume of the road network, provides an effective urban road network control means for the traffic manager, and improves the traffic service level of the urban road network.