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iterative learning control Algorithm with a Fixed Step

Chinese Journal of Mechanical Engineering 2011年第4期24卷 669-675页

作者： WANG Yan NIU Jianjun School of Transportation Science and Engineering Beihang University Beijing 100191 China International Petroleum Exploration and Production Corporation China Petroleum ＆ Chemical Corporation Beijing 100083 China

iterative learning control （ILC） captures interests of many scholars because of its capability of high precision control implement without identifying plant mathematical models, and it is widely applied in control engineering. Presently, most ILC algorithms still follow the original ideas of ARIMOTO, in which the iterative-learning-rate is composed by the control error with its derivative and integral values. This kind of algorithms will result in inevitable problems such as huge computation, big storage capacity for algorithm data, and also weak robust. In order to resolve these problems, an improved iterative learning control algorithm with fixed step is proposed here which breaks the primary thought of ARIMOTO. In this algorithm, the control step is set only according to the value of the control error, which could enormously reduce the computation and storage size demanded, also improve the robust of the algorithm by not using the differential coefficient of the iterative learning error. In this paper, the convergence conditions of this proposed fixed step iterative learning algorithm is theoretically analyzed and testified. Then the algorithm is tested through simulation researches on a time-variant object with randomly set disturbance through calculation of step threshold value, algorithm robustness testing,and evaluation of the relation between convergence speed and step size. Finally the algorithm is validated on a valve-serving-cylinder system of a joint robot with time-variant parameters. Experiment results demonstrate the stability of the algorithm and also the relationship between step value and convergence rate. Both simulation and experiment testify the feasibility and validity of the new algorithm proposed here. And it is worth to noticing that this algorithm is simple but with strong robust after improvements, which provides new ideas to the research of iterative learning control algorithms.

关键词： iterative learning control fixed step time variant system simulating study robot control

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iterative learning control for one-dimensional fourth order distributed parameter systems

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Science China(Information Sciences) 2017年第1期60卷 177-189页

作者： Qin FU Panpan GU Jianrong WU School of Mathematics and Physics Suzhou University of Science and Technology

This paper addresses the problem of iterative learning control algorithm for high order distributed parameter systems in the presence of initial errors. And the considered distributed parameter systems are composed of the one-dimensional fourth order parabolic equations or the one-dimensional fourth order wave equations. According to the characteristics of the systems, iterative learning control laws are proposed for such fourth order distributed parameter systems based on the P-type learning scheme. When the learning scheme is applied to the systems, the output tracking errors on 2L space are bounded, and furthermore, the tracking errors on 2L space can tend to zero along the iteration axis in the absence of initial errors. Simulation examples illustrate the effectiveness of the proposed method.

关键词： P-type learning scheme iterative learning control distributed parameter systems fourth order parabolic equation fourth order wave equation

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iterative learning control for Discrete Parabolic Distributed Parameter Systems

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International Journal of Automation and computing 2015年第3期12卷 316-322页

作者： Xi-Sheng Dai Sen-Ping Tian Ya-Jun Guo College of Electrical Engineering and Automation Shandong University of Science and Technology School of Electrical and Information Engineering Guangxi University of Science and Technology School of Automation Science and Engineering South China University of Technology

In this paper, iterative learning control(ILC) technique is applied to a class of discrete parabolic distributed parameter systems described by partial difference equations. A P-type learning control law is established for the system. The ILC of discrete parabolic distributed parameter systems is more complex as 3D dynamics in the time, spatial and iterative domains are *** overcome this difficulty, discrete Green formula and analogues discrete Gronwall inequality as well as some other basic analytic techniques are utilized. With rigorous analysis, the proposed intelligent control scheme guarantees the convergence of the tracking error. A numerical example is given to illustrate the effectiveness of the proposed method.

关键词： Intelligent control iterative learning control discrete systems distributed parameter systems convergence analysis.

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iterative learning control for nonlinear systems with uncertain state delay and arbitrary initial error

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控制理论与应用（英文版） 2011年第4期9卷 541-547页

作者： Hongbin WANG Yan WANG Key Laboratory of Industrial Computer control Engineering of Hebei Province Yanshan University Qinhuangdao Hebei 066004 China

Most of the existing iterative learning control algorithms proposed for time-delay systems are based on the condition that the time-delay is precisely available, and the initial state is reset to the desired one or a fixed value at the start of each operation, which makes great limitation on the practical application of corresponding results. In this paper, a new iterative learning control algorithm is studied for a class of nonlinear system with uncertain state delay and arbitrary initial error. This algorithm needs to know only the boundary estimation of the state delay, and the initial state is updated, while the convergence of the system is guaranteed. Without state disturbance and output measurement noise, the system output will strictly track the desired trajectory after successive iteration. Furthermore, in the presence of state disturbance and measurement noise, the tracking error will be bounded uniformly. The convergence is strictly proved mathematically, and sufficient conditions are obtained. A numerical example is shown to demonstrate the effectiveness of the proposed approach.

关键词： iterative learning control State-delay system Initial condition Uncertain disturbances

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iterative learning control for Discrete-Time Nonlinear Systems Based on Adaptive Tuning of 2D learning Gain 36

Iterative Learning Control for Discrete-Time Nonlinear Syste...

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第36届中国控制会议

作者： Xian Yu Zhongsheng Hou Chenkun Yin Advanced control Systems Laboratory School of Electronics and Information EngineeringBeijing Jiaotong University

ISBN: (纸本)9781538629185

A novel data-driven iterative learning control（ILC） based on the adaptive tuning of the 2D learning gain,is proposed in this paper for a class of general discrete-time nonlinear SISO *** on an equivalent compact form dynamic linearization data model of the controlled nonlinear system in the iteration domain,an iterative learning law is formulated by using a recursive search algorithm to adaptively tune the 2D learning gain with only the requirement of the measured I/O data of the controlled nonlinear *** theoretical analysis shows that the proposed ILC guarantees convergence of the tracking *** effectiveness of the proposed ILC is validated by simulations on a complex unknown nonlinear system with time-varying structure,order and parameters.

关键词： iterative learning control Compact Form Dynamic Linearization Recursive Search Algorithm Discrete-Time Nonlinear System

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iterative learning control for a Class of MIMO Nonlinear System with Input Saturation Constraint 36

Iterative Learning Control for a Class of MIMO Nonlinear Sys...

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第36届中国控制会议

作者： Zhang Ruikun Chi Ronghu School of Mathematics and Physics Qingdao University of Science and Technology School of Automation and Electronics Engineering Qingdao University of Science and Technology

ISBN: (数字)9789881563934

ISBN: (纸本)9781538629185

This paper presents an iterative learning control for a class of multiple-input multiple-output（MIMO）nonlinear parameterized system with input saturation *** dynamic function of the nonlinear system considered in this paper is described by an nonlinearly parameterized functions with input saturation *** considering the property of the saturation function in the MIMO nonlinear system,we design the P-type iterative learning controller with a feedback term and partially-saturated term and a fully-saturated parameter updating *** constructing a composite energy function（CEF）,we prove the convergence property of the tracking *** last,we give an numerical simulation which show that the control scheme proposed in this paper is effective and correct.

关键词： iterative learning control Input Saturation Constraint Multiple-Input Multiple-Output System

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iterative learning control for Linear Systems with Markov Data Dropouts:Noise-free Case 36

Iterative Learning Control for Linear Systems with Markov Da...

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第36届中国控制会议

作者： Dong Shen Jian-Xin Xu College of Information Science and Technology Beijing University of Chemical Technology Department of Electrical and Computer Engineering National University of Singapore

ISBN: (纸本)9781538629185

This paper contributes to the convergence analysis of iterative learning control(ILC) for a linear time-varying system with measurement data dropouts,where the data dropout problem is formulated by a Markov chain *** widely used Bernoulli model for data dropout is a special case of the Markov chain model.A regulating parameter is added to the classic P-type update *** mean square and almost sure convergence are strictly *** illustrative example is provided to verify the theoretical results.

关键词： iterative learning control Data Dropouts Markov Chain

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iterative learning control for Discrete Linear System with Wireless Transmission Based on Adaptive Fourier Decomposition 36

Iterative Learning Control for Discrete Linear System with W...

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第36届中国控制会议

作者： Ya Lei Yong Fang Liming Zhang IEEE School of Communication and Information Engineering Shanghai University Faculty of Science and Technology University of Macau

ISBN: (纸本)9781538629185

iterative learning control（ILC） is an important method for networked control systems and it uses the control information several times before to form the current control input signal[1].Nowadays,ILC has developed maturely,but its output signal may cannot track the desired trajectory accurately because of the channel noise of wireless *** this paper,we introduce the Adaptive Fourier Decomposition（AFD） algorithm to eliminate the channel noise superimposed on the output signal in the wireless transmission process[2].So an ILC method for discrete linear system with wireless transmission based on AFD in frequency domain is *** simulation results show that the AFD algorithm is able to achieve signal demising well in the case of small decomposition threshold compared with Fourier decomposition,and then the goal that the output signal of ILC system can track the desired signal accurately will be achieved.

关键词： iterative learning control Adaptive Fourier Decomposition Wireless transmission Frequency domain

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iterative learning control for a Class of Linear Continuous-time Switched Systems with Fixed Initial Shifts

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Communications in Mathematical Research 2018年第4期34卷 309-328页

作者： xu guang-zhao fu qin du li-li wu jian-rong yu peng-fei li yong School of Mathematics and Physics Suzhou University of Science and TechnologySuzhouJiangsu215009 不详

This paper deals with the problem of iterative learning control for a class of linear continuous-time switched systems in the presence of a fixed initial shift. Here, the considered switched systems are operated during a finite time interval repetitively. According to the characteristics of the systems, a PD-type learning scheme is proposed for such switched systems with arbitrary switching rules, and the corresponding output limiting trajectories under the action of the PD-type learning scheme are given. Based on the contraction mapping method, it is shown that this scheme can guarantee the outputs of the systems converge uniformly to the output limiting trajectories of the systems over the whole time interval. Furthermore, the initial rectifying strategies are applied to the systems for eliminating the effect of the fixed initial shift. When the learning scheme is applied to the systems, the outputs of the systems can converge to the desired reference trajectories over a pre-specified interval. Finally, simulation examples illustrate the effectiveness of the proposed method.

关键词： iterative learning control switched system PD-type learning scheme fixed initial shift output limiting trajectory

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CONVERGENCE ANALYSIS IN SENSE OF LEBESGUE-p NORM OF DECENTRALIZED NON-REPETITIVE iterative learning control FOR LINEAR LARGE-SCALE SYSTEMS

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Journal of Systems Science & Complexity 2009年第3期22卷 422-434页

作者： Xiaoe RUAN Huizhuo WU Na LI Baiwu WAN Department of Mathematics Faculty of Science Xi'an Jiaotong University Xi'an 710049 China. Institute of Systems Engineering Xi'an Jiaotong University Xi'an 710049 China.

In this paper,a decentralized iterative learning control strategy is embedded into theprocedure of hierarchical steady-state optimization for a class of linear large-scale industrial processeswhich consists of a number of *** task of the learning controller for each subsystem is toiteratively generate a sequence of upgraded control inputs to take responsibilities of a sequential stepfunctional control signals with distinct scales which are determined by the local decision-making units inthe two-layer hierarchical steady-state optimization *** objective of the designated strategyis to consecutively improve the transient performance of the *** means of the generalized Younginequality of convolution integral,the convergence of the learning algorithm is analyzed in the sense ofLebesgue-p *** is shown that the inherent feature of system such as the multi-dimensionality andthe interaction may influence the convergence of the non-repetitive learning *** simulationsillustrate the effectiveness of the proposed control scheme and the validity of the conclusion.

关键词： Convergence effectiveness iterative learning control large-scale systems Lebesgue-pnorm.

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