迭代学习控制的分析与仿真

迭代学习控制适合于具有重复运动性质的被控对象,通过迭代修正达到某种控制目标的改善。本文论述了迭代学习控制的基本原理,着重分析了迭代学习控制的算法,并讨论了其存在的问题以及仿真实例,仿真表明闭环迭代学习控制的收敛速度要好于开环迭代学习控制的收敛速度。     

迭代学习控制新进展

从学习算法、收敛性、鲁棒性、收敛速度、2-D模型以及实际应用等方面对迭代学习控制的最新进展作了比较详尽的总结, 最后讨论了迭代学习控制存在的问题及有前景的研究方向.     

迭代学习控制理论进展与展望

从迭代学习控制算法、鲁棒性、收敛速度以及实际应用等方面,对迭代学习控制的最新进展作了较详尽的总结,论述了迭代学习控制的基本原理,讨论了迭代学习控制存在的问题及展望.     

时滞系统采样迭代学习控制

针对一类具有状态时滞的连续系统提出一种采样迭代学习控制算法。给出并证明了算法指数收敛的充分条件,该条件可保证系统输出无论在采样点或非采样点上,都能以指数收敛速率收敛至期望输出的一个与采样周期有关的误差范围内。仿真结果表明了该算法的有效性。     

局部对称积分型迭代学习控制

提出了一个新的迭代学习控制（ＩＬＣ）更新律用于连续线性系统的有限时间区间跟踪控制,迭代学习控制作为一个前馈控制,迭代学习控制作为一个前馈控制器加在已有的反馈控制器之上,对于上倥 的反馈控制信号作局部对称积分,所提出的迭代学习控制更新律具备较简单的形式且仅含有两个设计参数,即：学习增益和局部积分的区间长度,给出了收敛性分析以及设计步骤。     

迭代学习控制系统的误差跟踪设计方法

提出能够实现期望误差轨迹完全跟踪的迭代学习控制系统设计方法, 旨在放宽常规迭代学习控制方法的初始定位条件, 在每次迭代时允许初值定位在任意位置. 这种方法对于预先给定的期望误差轨迹, 经迭代学习, 使得实际跟踪误差收敛于预定的误差轨迹, 这样, 预设的误差轨迹即最终形成的误差轨迹. 针对常参数、时变参数以及复合参数三种情形, 分别采用类Lyapunov方法设计迭代学习控制系统. 所设计的未含/含限幅作用的参数学习律, 能够使得跟踪误差轨迹在整个作业区间上与预定轨迹完全吻合, 并保证系统中所有信号的有界性. 给出的仿真结果表明所提方法的有效性.     

迭代学习控制系统的鲁棒收敛性分析(英)

本文提出一种开闭环配合的滤波器型选代学习控制算法，并将这种算法应用于一般非线性动态系统的轨迹跟踪．对于渐近重复初始条件和渐近周期干扰的情形，通过控制误差估计和输出误差估计，文中分别证明了学习过程的一致收敛性．证明中未采用线性化手段．     

Iterative Learning Control Utilizing the Error Prediction Method

In this paper, iterative learning control utilizing the error prediction method is proposed for a class of linear time varying systems subjected to disturbances. Prediction of the error is done by identifying the system time varying parameters. Convergence of the proposed method is analyzed and the uniform boundedness of tracking error is obtained in the presence of uncertainty and disturbances. It is shown that the learning algorithm not only guarantees the robustness, but also improves the learning rate despite the presence of disturbances. The effectiveness of the proposed method is presented by simulations.     

迭代学习控制在电力系统中的应用进展

根据迭代学习控制基本原理及其特性,归纳迭代学习控制在电力系统中的各种应用,对迭代学习控制的最新进展进行详尽的叙述,讨论迭代学习控制在电力系统中应用存在的问题,并对其发展进行展望.     

Iterative Learning Control With Incomplete Information: A Survey

This paper conducts a survey on iterative learning control (ILC) with incomplete information and associated control system design, which is a frontier of the ILC field. The incomplete information, including passive and active types, can cause data loss or fragment due to various factors. Passive incomplete information refers to incomplete data and information caused by practical system limitations during data collection, storage, transmission, and processing, such as data dropouts, delays, disordering, and limited transmission bandwidth. Active incomplete information refers to incomplete data and information caused by man-made reduction of data quantity and quality on the premise that the given objective is satisfied, such as sampling and quantization. This survey emphasizes two aspects: the first one is how to guarantee good learning performance and tracking performance with passive incomplete data, and the second is how to balance the control performance index and data demand by active means. The promising research directions along this topic are also addressed, where data robustness is highly emphasized. This survey is expected to improve understanding of the restrictive relationship and trade-off between incomplete data and tracking performance, quantitatively, and promote further developments of ILC theory.      

Iterative Learning Control for Discrete-time Stochastic Systems with Quantized Information

An iterative learning control (ILC) algorithm using quantized error information is given in this paper for both linear and nonlinear discrete-time systems with stochastic noises. A logarithmic quantizer is used to guarantee an adaptive improvement in tracking performance. A decreasing learning gain is introduced into the algorithm to suppress the effects of stochastic noises and quantization errors. The input sequence is proved to converge strictly to the optimal input under the given index. Illustrative simulations are given to verify the theoretical analysis.      

离散非线性系统开闭环P型迭代学习控制律及其收敛性

本文在讨论了一般开环与闭环迭代学习控制的不足后，针对一类离散非线性系统，提出了新的开闭环ＰＧ型迭代学习控制律，给出了它的收敛性证明，仿真结果表明：开闭环Ｐ型迭代律优于单纯的开环或产才环Ｐ型迭代 律。     

一类广义系统的迭代学习控制

在对广义系统进行标准分解的基础上, 研究了含脉冲快子系统的迭代学习控制问题. 通过 Frobenius 范数给出了快子系统在 P 型学习律作用下收敛的充分性条件, 同时通过梯度法给出求解增益矩阵的方法. 其次, 讨论了单输入单输出不确定广义系统的迭代学习控制问题, 通过优化方法给出该系统在 P 型学习律作用下, 系统实际输出尽可能快地收敛到理想输出的增益矩阵的选择方法.     

D型迭代学习控制及其在FNS肢体运动控制系统中的应用

给出了离散系统D－型迭代学习控制算法收敛的一种充分条件,并加以证明。采用D型迭代学习控制算法,对基于功能性神肉电刺激的曲腕和曲肘运动进行了临床实验研究,结果表明,D型迭代学习算法改善了FNS肢体运动控制的跟踪性能,曲肘和曲腕运动轨迹平滑、稳定,并且刺激控制脉冲变化平缓,受试者无任何不良生理反应。     

Prediction-based Iterative Learning Control (PILC) for Uncertain Dynamic Nonlinear Systems Using System Identification Technique

Prediction-based Iterative Learning Control (PILC) is proposed in this paper for a class of time varying nonlinear uncertain systems. Convergence of PILC is analyzed and the uniform boundedness of tracking error is obtained in the presence of uncertainty and disturbances. It is shown that the learning algorithm not only guarantees the robustness, but also improves the learning rate despite the presence of disturbances and slowly varying desired trajectories in succeeding iterations. The effectiveness of the proposed PILC is presented by simulations.     

Stochastic Iterative Learning Control With Faded Signals

Stochastic iterative learning control (ILC) is designed for solving the tracking problem of stochastic linear systems through fading channels. Consequently, the signals used in learning control algorithms are faded in the sense that a random variable is multiplied by the original signal. To achieve the tracking objective, a two-dimensional Kalman filtering method is used in this study to derive a learning gain matrix varying along both time and iteration axes. The learning gain matrix minimizes the trace of input error covariance. The asymptotic convergence of the generated input sequence to the desired input value is strictly proved in the mean-square sense. Both output and input fading are accounted for separately in turn, followed by a general formulation that both input and output fading coexists. Illustrative examples are provided to verify the effectiveness of the proposed schemes.      

迭代学习控制的研究与现状

迭代学习控制适用于工业机器人、数控机床等具有重复运行特性的领域,在非线性、未知模型等系统的控制方面有着独到优势。本文论述了迭代学习控制的基本理论问题,系统介绍了理论研究现状及工程应用,并讨论了其存在的问题和发展趋势。     

Convergence Analysis of Wireless Remote Iterative Learning Control Systems with Channel Noise

Channel noise, including sensor‐to‐controller(SC) noise and controller‐to‐actuator(CA) noise, impacts the convergence of wireless remote iterative learning control (ILC) system significantly. In this paper, the relationship between output error, SC noise and CA noise is obtained firstly by super‐vector formulation, and then the norm of output error vector covariance matrix is employed to analyze the convergence of the system in presence of SC noise and CA noise. Upper bound of the norm at any sample time reveals that the SC noise is accumulated only in iteration domain, while the CA noise is accumulated not only in iteration domain but also in time domain. Furthermore, the accumulated effect of the CA noise in time domain is ruled by system matrices, so the values of which determine the effect of the CA noise is greater or less than that of the SC noise on convergence of the system. Finally, some simulation results are given to illustrate correctness of the result.     

迭代学习控制研究现状与趋势

系统论述了迭代学习控制的发展历史、研究进展.指出了基于可重复性的经典迭代学习控制特点与不足,阐述了迭代学习控制理论的现状:线性与非线性迭代学习、因果与非因果型迭代学习、滤波器型与鲁棒H∞迭代学习、高阶与最优迭代学习、2D复合迭代学习、迭代域超级矢量w变换学习系统分析理论等.简要介绍了与Lyapunov方法结合的新迭代学习控制,最后讨论了迭代学习控制存在问题和发展趋势.同时给出了几个迭代学习控制在工程应用中的成功范例.     

提高迭代自学习控制算法收敛速度初探

从学习律、学习律参数、输出误差等三方面讨论了迭代自学习算法的收敛速度,为提高该算法的收剑速度得到了一些有用的结论。