非线性时滞大系统自适应神经网络分散控制

针对一类未知非线性时滞关联大系统，提出一种自适应神经网络分散跟踪控制方案．采用神经网络逼近各子系统内部的非线性函数和关联项中的时滞非线性函数；利用占有方法处理时滞项，采用Backstepping技术设计分散控制律和参数自适应律．基于Lyapunov-Krasoviskii泛函证明了闭环大系统所有信号半全局一致最终有界．通过调节设计参数和增加神经元个数，可以实现任意输出跟踪精度．实例仿真说明了该方案的可行性。     

关联系统的分散自适应控制

本文研究了一类大系统的分散自适应控制,导出了一组鲁棒性较强的自适应控制律.当 各子系统间存在参数未知的非线性的任意关联和有界外扰时,若系统相关阶次小于或等于2r 那么状态和参数误差以指数收敛到某有界余集.在外扰为常数时,输出能实现完全跟踪.     

一类分布式系统的自适应跟踪和扰动抑制

研究直接自适应状态反馈控制策略解决一类有故障和摄动关联链接的分布式大系统渐进跟踪和扰动抑制问题. 根据特殊的分布式结构, 在所有关联故障因子, 关联通道摄动和子系统外部干扰的上界都未知下, 提出自适应率在线升级控制器参数. 基于自适应策略信息, 构造一类分布式状态反馈控制器自动补偿故障和摄动影响, 同时抑制外部扰动. 在关联通道有故障摄动和外部扰动情况下, 所提出的自适应鲁棒跟踪控制器可以保证所得自适应闭环大系统稳定, 及每个子系统渐进输出跟踪所对应参考信号. 最后由一个仿真例子评估所提技术的有效性.     

离散大系统存在未模化动力学时的分散自适应控制

本文研究了离散大系统存在未模化动力学时的分散模型参考自适应控制问题。对以状态变量和输入-输出变量设计的自适应系统,得到了复合闭环系统的所有信号及跟踪误差将收敛并保持在一有界集的充分条件。     

一类不确定非线性大系统的分散鲁棒控制器设计

基于Backstepping方法，设计了一类具有不确定性扰动和不确定性关联项的非线性大系统的分散鲁棒稳定控制器。非线性大系统的关联项为时变有界非线性函数且不确定性扰动以仿射非线性方程的形式引入。为了提高系统的控制效果。将：Backstepping递推设计方法与L2增益控制相结合，所设计的分散鲁棒控制器不仅使每个子系统的状态向量跟踪一个指定的期望轨迹。而且还使系统的不确定性干扰具有L2增益控制。     

分散混杂自适应模糊H∞控制器及其在自动化公路系统中的应用

本文针对一类SISO不确定非线性大系统,提出了一种混杂间接和直接自适应分散模糊H∞控制器.通过组合模糊系统和H∞跟踪技术开发的分散自适应模糊控制算法避免了控制设计中含有的符号函数.两种自适应模糊控制器的组合消除了它们各自均不能够同时融合被控对象知识与控制知识的局限.闭环大系统被证明是稳定的,且具有H∞跟踪性能.该算法应用于自动化公路系统中车辆的纵向跟随控制,仿真结果表明混杂自适应模糊H∞控制系统的跟踪性能更好而相应的控制幅值却更小.     

具有不确定未知界的相似组合系统的鲁棒自适应跟踪控制

讨论一类具有相似结构的不确定组合系统的鲁棒自适应跟踪问题。针对系统的不确定性界和扰动界完全未知的情形，首先从理论上证明了可设计鲁棒自适应分散跟踪控制器，确保受控系统的输出渐近跟踪参考模型的输出；进而从工程实际的角度，给出了确保受控系统输出实用跟踪参考模型输出的鲁棒自适应分散跟踪控制器的设计方案。     

不确定性大系统的分散变结构自适应控制

提出一种适用于存在变化的子系统关联、系统参数摄动和外界干扰的不确定性线性大系统的分散变结构自适应控制方法。在控制律中引入一种对系统不确定扰动与关联的界的简单自适应算法，增强了控制系统对不可知系统不确定扰动的鲁棒性。所提出的控制方法具有较强的工程实用性。     

基于线性矩阵不等式的分散鲁棒跟踪控制器设计

应用线性矩阵不等式（ＬＭＩ）方法研究不确定性关联大系统的分散鲁棒输出跟踪控制问题。系统中不确定项具有数值界,可不满足匹配条件。基于不确定基项的表达形式,给出了存在分散控制鲁棒跟踪控制器的ＬＭＩ条件。在此基础上,通过建立求解受ＬＭＩｓ约束的凸优化问题,提出了具有较小反馈增益ＬＭＩ设计方法,使受控系统渐近跟踪给定的参考输入。ＬＭＩ方法求解简单,最后用示例说明了该方法的应用。     

直线电机位置轨迹自适应渐近跟踪

针对直线电机构成的伺服系统中存在的负载扰动和端部效应等阻力扰动，设计了一个自适应的轨迹渐近跟踪控制器；控制律的实现只涉及输出位移的测量信号，而不必依赖于速度的测量；先引入一个基于K滤波器结构的降阶观测器，接着利用自适应观测器后推技术设计了轨迹跟踪控制器和扰动参数估计的自适应律；对闭环系统的稳定性和渐近跟踪能力做了理论分析，并进行了仿真研究；仿真结果表明；设计的控制器可以实现对目标轨迹的渐近跟踪；在系统持续激励的情况下，扰动参数的估计可以收敛到真正的值。     

分散模型参考自适应控制

本文针对由参数未知、存在有界扰动和非线性关联的子系统组成的大规模互联系统,提出 了一种新的分散模型参考自适应控制法.它适用于孤立子系统传递函数的相对阶次n*i为任 意值的情况.根据李雅普诺夫稳定性理论,文中证明了这种分散自适应控制系统全局稳定的 充分条件.与有关文献所介绍的方法相比,本文的方法可用于n*i>2的场合,因而它更具有 一般性和实用性.     

Adaptive fuzzy decentralized control for interconnected MIMO nonlinear subsystems

This paper describes an adaptive fuzzy control strategy for decentralized control for a class of interconnected nonlinear systems with MIMO subsystems. An adaptive robust tracking control schemes based on fuzzy basis function approach is developed such that all the states and signals are bounded. In addition, each subsystem is able to adaptively compensate for disturbances and interconnections with unknown bounds. The resultant adaptive fuzzy decentralized control with multi-controller architecture guarantees stability and convergence of the output errors to zero asymptotically by local output-feedback. An extensive application example of a three-machine power system is discussed in detail to verify the effectiveness of the proposed algorithm.     

Decentralized adaptive iterative learning control for interconnected systems with uncertainties

In many applications,the system dynamics allows the decomposition into lower dimensional subsystems with interconnections among them.This decomposition is motivated by the ease and flexibility of the controller design for each subsystem.In this paper,a decentralized model reference adaptive iterative learning control scheme is developed for interconnected systems with model uncertainties.The interconnections in the dynamic equations of each subsystem are considered with unknown boundaries.The proposed controller of each subsystem depends only on local state variables without any information exchange with other subsystems.The adaptive parameters are updated along iteration axis to compensate the interconnections among subsystems.It is shown that by using the proposed decentralized controller,the states of the subsystems can track the desired reference model states iteratively.Simulation results demonstrate that,utilizing the proposed adaptive controller,the tracking error for each subsystem converges along the iteration axis.     

严格反馈互联系统分散式backstepping自适应迭代学习控制

基于Lyapunov分析方法,针对具有严格反馈形式的非线性互联系统,本文设计了一种分散式backstepping自适应迭代学习控制器.子系统之间的互联项为所有子系统输出项线性有界,为每个子系统设计的控制器仅采用该子系统的信息,不需要子系统之间相互传递信息.在控制器中,引入在时间轴和迭代轴上同时更新的自适应参数,以补偿子系统之间的互联项影响.通过采用本文给出的控制器,可使得每个子系统的输出跟踪相应的参考模型输出,仿真结果验证了本文算法的有效性.     

Decentralized output-feedback MRAC of linear state delay systems

In this note, we develop coordinated decentralized output-feedback adaptive controllers for a class of large-scale systems with state time delays in the subsystems and in the interconnections. We present a decentralized model reference adaptive control scheme which requires an exchange of signals between the different reference models, but does not involve the exchange of output signals between the different subsystems. It can not only guarantee closed-loop stability but also asymptotic zero tracking errors when uncertainties and delays are present in the subsystems and interconnections. Closed-loop signal boundedness and asymptotic output-feedback tracking are proven analytically and verified by simulation.     

关联不确定大系统的分散变结构控制

分散控制方法和变结构控制方法两者优点的结合使得分散变结构控制在大系统研 究中得到了广泛的重视.针对各个子系统均为多输入情况.研究了不确定性关联大系统的分 散变结构控制方法.基于开关平面的等价性,提出了关联大系统的分散滑动模态全局可达条 件.并针对子系统不确定性的界已知及未知两种情况,提出了分散变结构控制算法.该方法克 服了以往控制方法中需已知线性关联函数或不确定性关联的界的缺陷.     

Adaptive fuzzy semi‐decentralized control for a class of large‐scale nonlinear systems with unknown interconnections

In this paper, a general method is developed to generate a stable adaptive fuzzy semi‐decentralized control for a class of large‐scale interconnected nonlinear systems with unknown nonlinear subsystems and unknown nonlinear interconnections. In the developed control algorithms, fuzzy logic systems, using fuzzy basis functions (FBF), are employed to approximate the unknown subsystems and interconnection functions without imposing any constraints or assumptions about the interconnections. The proposed controller consists of primary and auxiliary parts, where both direct and indirect adaptive approaches for the primary control part are aiming to maintain the closed‐loop stability, whereas the auxiliary control part is designed to attenuate the fuzzy approximation errors. By using Lyapunov stability method, the proposed semi‐decentralized adaptive fuzzy control system is proved to be globally stable, with converging tracking errors to a desired performance. Simulation examples are presented to illustrate the effectiveness of the proposed controller. Copyright © 2006 John Wiley & Sons, Ltd.     

Decentralized adaptive control of a class of large-scaleinterconnected nonlinear systems

Decentralized adaptive control design for a class of large-scale interconnected nonlinear systems with unknown interconnections is considered. The motivation behind this work is to develop decentralized control for a class of large-scale systems which do not satisfy the matching condition requirement. To this end, large-scale nonlinear systems transformable to the decentralized strict feedback form are considered. Coordinate-free geometric conditions under which any general interconnected nonlinear system can be transformed to this form are obtained. The interconnections are assumed to be bounded by polynomial-type nonlinearities. Global stability and asymptotic regulation are established using classical Lyapunov techniques. The controller is shown to maintain robustness for a wide class of systems obtained by perturbation in the dynamics of the original system. Furthermore, appending additional subsystems does not require controller redesign for the original subsystems. Finally, the scheme is extended to the model reference tracking problem when global uniform boundedness of the tracking error to a compact set is established     

Adaptive neural decentralized control for strict feedback nonlinear interconnected systems via backstepping

An approximation based adaptive neural decentralized output tracking control scheme for a class of large-scale unknown nonlinear systems with strict-feedback interconnected subsystems with unknown nonlinear interconnections is developed in this paper. Within this scheme, radial basis function RBF neural networks are used to approximate the unknown nonlinear functions of the subsystems. An adaptive neural controller is designed based on the recursive backstepping procedure and the minimal learning parameter technique. The proposed decentralized control scheme has the following features. First, the controller singularity problem in some of the existing adaptive control schemes with feedback linearization is avoided. Second, the numbers of adaptive parameters required for each subsystem are not more than the order of this subsystem. Lyapunov stability method is used to prove that the proposed adaptive neural control scheme guarantees that all signals in the closed-loop system are uniformly ultimately bounded, while tracking errors converge to a small neighborhood of the origin. The simulation example of a two-spring interconnected inverted pendulum is presented to verify the effectiveness of the proposed scheme.     

Robust decentralized model reference adaptive control of discrete-time interconnected systems

A robust decentralized model reference adaptive controller is proposed for a class of large-scale systems composed of several interconnected subsystems and described by state space equations. We have formulated a local adaptive controller for each subsystem using only local information such that the state of this subsystem tracks the corresponding state of a reference model. The content of the paper is limited to interconnected subsystems which are described by linear, deterministic, single-input single-output and discrete-time models with unknown and/or slowly time-varying parameters. Sufficient conditions, formulated by utilizing Lyapunov theory, are given for the overall system to be stabilizable by decentralized state feedback adaptive control laws. The results are illustrated by a numerical example.