非线性系统高阶迭代学习算法

结合迭代学习控制算法中的开环和闭环方案，本文针对更一般的非线性系统，讨论高阶算法的广泛适用性。理论和仿真结果表明了高阶算法在输出跟踪和干扰抑制方面的有效性。     

P-type Iterative Learning Control with Initial State Learning for One-sided Lipschitz Nonlinear Systems

International Journal of Control, Automation and Systems - In this paper, the problem of iterative learning control is considered for a class of one-sided Lipschitz nonlinear systems. For such...     

非线性非仿射离散时间系统的两阶段最优迭代学习控制

On the basis of a new dynamic linearization technology along the iteration axis,a dual-stage optimal iterative learning control is presented for nonlinear and non-affine discrete-time systems.Dual-stage indicates that two optimal learning stages are designed respectively to improve control input sequence and the learning gain iteratively.The main feature is that the controller design and convergence analysis only depend on the I/O data of the dynamical system.In other words,we can easily select the control parameters without knowing any other knowledge of the system.Simulation study illustrates the geometrical convergence of the presented method along the iteration axis,in which an example of freeway traffic iterative learning control is noteworthy for its intrinsic engineering importance.     

A Unified Adaptive Iterative Learning Control Framework for Uncertain Nonlinear Systems

In this note, we propose a unified framework for adaptive iterative learning control design for uncertain nonlinear systems. It is shown that if a Lyapunov based adaptive control law is available for the system under consideration and the Lyapunov function satisfies certain conditions, it is straightforward to extend the adaptive controller to handle repetitive systems operating over a finite time interval. According to the value of a certain parameter gamma, the parametric adaptation law can be a pure time-domain adaptation, a pure iteration-domain adaptation or a combination of both.A pure iteration-domain adaptation is described by a difference equation, a pure time-domain adaptation is described by a differential equation, and a combination of both is described by a differential-difference equation. The advantages and disadvantages of the three possible adaptation types are discussed and some illustrative examples are given. [All rights reserved Elsevier].     

一类非线性相似组合大系统的迭代学习控制

利用状态反馈部分线性化技术研究了一类非线性相似组合大系统的迭代学习控制问题。与现有结果不同的是，它不直接研究系统本身，而是构造一个适当的 反馈，然后对闭环系统给出其迭代学习收敛的充分条件，     

保密通信中混沌遮掩法的实现方案研究

提出由一阶时延微分方程描述的无限维混沌系统，在结构上易于实现。本文将有用信号直接输入该系统以实现保密通信，理论推导和实验结果均表明在经历很短的过渡时间后，接收端和发射端能很快同步。本文的模型在很大参数范围内都能产生混沌，因此具有良好的保密性。     

非线性系统迭代跟踪控制的批次遗忘学习算法

针对P型迭代学习算法对初始偏差和输出误差扰动敏感,以及PD型迭代学习算法容易放大系统噪声,降低系统鲁棒性的问题,研究了具有任意有界扰动及期望输出的重复运行非线性时变系统的PD型迭代学习跟踪控制算法.利用迭代学习过程记忆的期望轨迹、期望控制以及跟踪误差,给出基于变批次遗忘因子的学习控制器设计,并借助λ范数理论和Bellman-Gronwall不等式,讨论保证闭环跟踪系统批次误差有界的学习增益存在的充分必要条件,及分析控制算法的一致收敛性.本算法改善了系统的鲁棒性和动态特性,单关节机械臂的跟踪控制仿真验证了方法的有效性.     

基于SSL的Agent安全通信

Agent平台之间通过公共通信设施进行通信。Agent和通信服务协议自身不提供加密和签名等功能,这给Agent的通信带来了威胁。SSL是一种在客户端和服务器端建立安全通道的协议,通过对SSL协议的分析,认为SSL的加密套件与身份认证安全机制可以为Agent平台之间的通信提供安全通道,提出了将SSL协议架设在Agent平台之下通信协议之上的安全通信方案,给出了基于SSL协议的Agent通信代码,保证了Agent平台间的通信安全。     

Robust Discrete-Time Iterative Learning Control for Nonlinear Systems With Varying Initial State Shifts

This note is concerned with the robust discrete-time iterative learning control (ILC) design for nonlinear systems with varying initial state shifts. A two-gain ILC law is considered using a 2-D analysis approach. Sufficient conditions are derived to guarantee both convergence of the learning process for fixed initial condition and boundedness of the tracking error for variable initial condition. It is shown that the error data with anticipation in time can well handle the varying initial state shifts in discrete-time ILC.      

Analysis of Nonlinear Discrete-Time Systems with Higher-Order Iterative Learning Control

In this paper, an iterative learning control method is proposed for a class of nonlinear discrete-time systems with well-defined relative degree, which uses the output data from several previous operation cycles to enhance tracking performance. A new analysis approach is developed, by which the iterative learning control is shown to guarantee the convergence of the output trajectory to the desired one within bound and the bound is proportional to the bound on resetting errors. It is further proved effective to overcome initial shifts and the resultant output trajectory can be assessed as iteration increases. Numerical simulation is carried out to verify the theoretical results and exhibits that the proposed updating law possesses good transient behavior of learning process so that the convergence speed is improved.     

非线性时滞系统的周期自适应学习跟踪控制

针对一类参数未知的周期非线性时滞系统的输出跟踪控制问题,设计了一种周期自适应迭代学习跟踪控制算法,该方法利用信号置换的思想重组系统,并在假设未知时变参数和参考输出的周期具有已知最小公倍数的情况下,将时滞以及其他不确定的时变项合并为一个周期性的辅助时变参数新变量,进而用周期自适应算法来估计该辅助量.通过构造一个Lyapunov-Krasovskii型复合能量函数,分析了系统的收敛性,证明了经过多次重复迭代学习,所有闭环信号有界且输出跟踪误差收敛,最后通过构造数值实例进行了仿真验证.理论分析和仿真结果表明,该算法简单有效,对于非线性时滞系统的跟踪问题具有很好的控制效果.     

时变非线性系统迭代学习控制遗忘因子算法

针对迭代学习P型控制算法对初始偏差和输出误差扰动的敏感性问题,研究了一种带有遗忘因子的时变非线性系统的迭代学习控制方法.在有扰动的情况下,利用迭代学习过程记忆的期望轨迹,期望控制以及跟踪误差,通过有界学习增益和批次时变因子设计学习控制器,并基于算子理论给出了控制算法存在的充分必要条件及其收敛性分析,改善了系统的鲁棒性和动态特性.最后以注塑机的注射速度控制仿真验证了本文算法的有效性.     

基于模糊网络的非线性不确定系统的迭代学习控制

针对非线性不确定系统，为保证受控制系统的响应特性，应用模糊网络理论构建了非线性系统的模糊模型；基于模糊模型给出了模糊迭代学习控制算法．实例分析和仿真验证表明，所给算法能跟踪系统参数变化，并且能自动调整控制增益，对于系统的扰动影响具有鲁棒性，是一种行之有效的控制方案．     

Normalized Learning Rule for Iterative Learning Control

International Journal of Control, Automation and Systems - The iterative learning control (ILC) is attractive for its simple structure, easy implementation. So the ILC is applied to various fields....     

Iterative Learning Control for Nonlinear Systems with Data Dropouts at Both Measurement and Actuator Sides

This paper discusses the iterative learning control (ILC) for nonlinear systems under a general networked control structure, in which random data dropouts occur independently at both measurement and actuator sides. Both updating algorithms are proposed for the computed input signal at the learning controller and the real input signal at the plant, respectively. The system output is strictly proved to converge to the desired reference with probability one as the iteration number goes to infinity. A numerical simulation is provided to verify the effectiveness of the proposed mechanism and algorithms.     

Adaptive Iterative Learning Control of Non-uniform Trajectory Tracking for Strict Feedback Nonlinear Time-varying Systems

In this paper, an iterative learning control strategy is presented for a class of nonlinear time-varying systems, the timevarying parameters are expanded into Fourier series with bounded remainder term. The backstepping design technique is used to deal with system dynamics with non-global Lipschitz nonlinearities and the approach proposed in this paper solves the non-uniform trajectory tracking problem. Based on the Lyapunov-like synthesis, the proposed method shows that all signals in the closed-loop system remain bounded over a pre-specified time interval [0, T ]. And perfect non-uniform trajectory tracking of the system output is completed. A typical series is introduced in order to deal with the unknown bound of remainder term. Finally, a simulation example shows the feasibility and effectiveness of the approach.     

Secure query processing against encrypted XML data using Query-Aware Decryption

Dissemination of XML data on the internet could breach the privacy of data providers unless access to the disseminated XML data is carefully controlled. Recently, the methods using encryption have been proposed for such access control. However, in these methods, the performance of processing queries has not been addressed. A query processor cannot identify the contents of encrypted XML data unless the data are decrypted. This limitation incurs overhead of decrypting the parts of the XML data that would not contribute to the query result. In this paper, we propose the notion of Query-Aware Decryption for efficient processing of queries against encrypted XML data. Query-Aware Decryption allows us to decrypt only those parts that would contribute to the query result. For this purpose, we disseminate an encrypted XML index along with the encrypted XML data. This index, when decrypted, informs us where the query results are located in the encrypted XML data, thus preventing unnecessary decryption for other parts of the data. Since the size of this index is much smaller than that of the encrypted XML data, the cost of decrypting this index is negligible compared with that for unnecessary decryption of the data itself. The experimental results show that our method improves the performance of query processing by up to six times compared with those of existing methods. Finally, we formally prove that dissemination of the encrypted XML index does not compromise security.     

An Iterative Learning Control of Nonlinear Systems Using Neural Network Design

In this paper, a feedforward neural network with sigmoid hidden units is used to design a neural network based iterative learning controller for nonlinear systems with state dependent input gains. No prior offline training phase is necessary, and only a single neural network is employed. All the weights of the neurons are tuned during the iteration process in order to achieve the desired learning performance. The adaptive laws for the weights of neurons and the analysis of learning performance are determined via Lyapunov‐like analysis. A projection learning algorithm is used to prevent drifting of weights. It is shown that the tracking error vector will asymptotically converges to zero as the iteration goes to infinity, and the all adjustable parameters as well as internal signals remain bounded.     

非线性动态滤波的迭代算法

阐述了标称状态的线性化方法和扩展的卡尔曼滤波公式及迭代卡尔曼滤波,探讨了非线性动态滤波的近似处理方法,围绕标称状态将非线性模型进行线性化,将标准的卡尔曼滤波扩展到非线性模型,得到扩展的卡尔曼滤波公式,研究了迭代滤波计算方法。扩展的卡尔曼滤波方法已经有效地用于非线性模型。