Decentralized adaptive tracking control of nonaffine nonlinear large-scale systems with time delays

This paper addresses the problem of decentralized tracking control of large-scale systems with uncertain nonaffine nonlinear isolated subsystems and nonlinear interconnections with time-varying delays. Based on Lyapunov-Krasovskii functional approach and implicit function theorem, a delay-independent decentralized tracking controller is proposed. Due to functional approximation capability of fuzzy logic systems (FLS), neither strict structure restrictions on the isolated subsystems nor a priori knowledge of the strong interconnections with time-varying delays is required in our control design. Furthermore, transient performance of the resulting closed-loop system is also addressed under an analytical framework. Finally, two numerical examples are provided to show the effectiveness of the proposed controller.     

Decentralized global robust stabilization of a class of interconnected minimum-phase nonlinear systems

This paper focuses on a class of large-scale interconnected minimum-phase nonlinear systems with parameter uncertainty and nonlinear interconnections. The uncertain parameters are allowed to be time-varying and enter the systems nonlinearly. The interconnections are bounded by nonlinear functions of states. The problem we address is to design a decentralized robust controller such that the closed-loop large-scale interconnected nonlinear system is globally asymptotically stable for all admissible uncertain parameters and interconnections. It is shown that decentralized global robust stabilization of the system can be achieved using a control law obtained by a recursive design method together with an appropriate Lyapunov function.     

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。     

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     

Decentralized state-feedback stabilization and robust control of uncertain large-scale systems with integrally constrained interconnections

This paper is concerned with a problem of stabilization and robust control design for interconnected uncertain systems. A new class of uncertain large-scale systems is considered in which interconnections between subsystems as well as uncertainties in each subsystem are described by integral quadratic constraints. The problem is to design a set of local (decentralized) controllers which stabilize the overall system and guarantee robust disturbance attenuation in the presence of the uncertainty in interconnections between subsystems as well as in each subsystem. The paper presents necessary and sufficient conditions for the existence of such a controller. The proposed design is based on recent absolute stabilization and minimax optimal control results and employs solutions of a set of game-type Riccati algebraic equations arising in H_∞ control.     

Non-affine nonlinear adaptive control of decentralized large-scale systems using neural networks

This paper introduces a new decentralized adaptive neural network controller for a class of large-scale nonlinear systems with unknown non-affine subsystems and unknown interconnections represented by nonlinear functions. A radial basis function neural network is used to represent the controller’s structure. The stability of the closed loop system is guaranteed through Lyapunov stability analysis. The effectiveness of the proposed decentralized adaptive controller is illustrated by considering two nonlinear systems: a two-inverted pendulum and a turbo generator. The simulation results verify the merits of the proposed controller.     

Decentralized control of a class of large-scale nonlinear systems using neural networks

This paper designs a decentralized neural network (NN) controller for a class of nonlinear large-scale systems, in which strong interconnections are involved. NNs are used to handle unknown functions. The proposed scheme is proved guaranteeing the boundedness of the closed-loop subsystems using only local feedback signals.     

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.     

关联大系统的分散自适应控制：孤立子系统动力学已知情况

本文研究了一类关联大系统的分散自适应控制问题，拓广了分散自适应控制的应用范围，在孤立子系统（ＭＩＭＯ）动力学信息已知，关联强度未知的情况下，论文提出了结构相当简单的分散自适应控制策略，它可保证闭环的分散自适应控制系统具有良好的动力学行为；系统的所有信号都全局有界，进一步，在状态调节情况下，关联大系统状态趋向于零；在模型跟踪情况下，跟踪误差可以通过控制器的设计使之任意小。     

关联大系统的分散H∞/LTR控制

讨论关联大系统分散回路传递再生的设计问题.利用矩阵的奇异值分解技术,提出了对 关联项进行块对角化处理的一种新方法.基于H∞理论,将多变量系统的回路传递再生方法 推广于分散控制关联大系统,并避免了在所有可能的分散观测器增益构成的集合上,直接计算 再生矩阵的H∞范数下确界的困难.     

RBFN-based decentralized adaptive control of a class of large-scale non-affine nonlinear systems

For a class of large-scale decentralized nonlinear systems with strong interconnections, a radial basis function neural network (RBFN) adaptive control scheme is proposed. The system is composed of a class of non-affine nonlinear subsystems, which are implicit function and smooth with respect to control input. Based on implicit function theorem, inverse function theorem and the design idea of pseudo-control, a novel control algorithm is proposed. Two neural networks are used to approximate unknown nonlinearities in the subsystem and unknown interconnection function, respectively. The stability is proved rigidly. The result of simulation validates the effectiveness of the proposed scheme.     

Decentralized control design for large-scale systems with strong interconnections using neural networks

We propose a decentralized neural network (NN) controller for a class of large-scale nonlinear systems with the strong interconnections. The NNs are used to approximate the unknown subsystems and interconnections. Due to the functional approximation capabilities of NNs, the additional precautions are not required to be made for avoiding the possible control singularity problems. Semiglobal asymptotic stability results are obtained and the tracking error converges to zero. Furthermore, the issue of transient performance of the subsystems is also addressed under an analytical framework.     

Output feedback decentralized control of large-scale systems using weighted sensitivity functions minimization

This paper considers the problem of achieving stability and desired dynamical transient behavior for linear large-scale systems, by decentralized control. It can be done by making the effects of the interconnections between the subsystems arbitrarily small. Sufficient conditions for stability and diagonal dominance of the closed-loop system are introduced. These conditions are in terms of decentralized subsystems and directly make a constructive H_∞ control design possible. A mixed H_∞ pole region placement is suggested, such that by assigning the closed-loop eigenvalues of the isolated subsystems appropriately, the eigenvalues of the overall closed-loop system are assigned in desirable range. The designs are illustrated by an example.     

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.     

一类关联大系统的分散强镇定

研究由子系统通过输入输出通道实现连接的关联大系统的分散强镇定问题，对于静态连接或的动态连接的情形，给出了大系统可以实现分散强镇定的条件，这些条件的检验只在子系统一级进行，具有较低的计算维数。     

含非匹配互联项的一类大型互联非线性系统的鲁棒分散控制

本文对带有界扰动的一类含非匹配互联项的大型互联非线性系统进行了分散状态反馈控制设计, 通过子系统状态的线性变换, 得到分散状态反馈控制律. 当状态反馈控制律作用于该系统时, 无扰动的闭环系统是全局渐近稳定的, 当扰动有界时, 系统的状态能够收敛到原点的一个与扰动的界相关的邻域内, 并给出仿真算例说明该结论的可行性和有效性.     

跳变双线性随机离散组合系统的保成本分散控制

研究一类跳变双线性随机离散组合系统的保成本分散控制问题．首先给出问题可解的充分条件,然后基于线性矩阵不等式方法设计保成本分散状态反馈控制律．理想的保成本分散状态反馈控制器可通过应用现有的软件，求解一组线性矩阵不等式而得到．仿真例子说明了该方法的有效性.     

Lurie控制系统的关联绝对稳定性-双线性矩阵不等式方法

研究任意两个相互独立的Lurie控制系统能否通过关联或协调控制组成绝对稳定大系统的问题,给出两个Lurie控制系统可关联绝对稳定的充分条件,并给出了计算关联矩阵的双线性矩阵不等式方法.研究结果表明:两个非绝对稳定的系统可以通过关联或协调控制来实现关联大系统的绝对稳定性,取消了大系统稳定性分析中对关联的不合理限制.文末给出了本文结果的数值例子.     

非线性扩展结构大系统自适应神经网络跟踪控制

针对一类非线性关联大系统在结构扩展时的跟踪控制问题, 提出一种采用自适应神经网络的控制方法. 该方法要求在不改变原结构系统控制律的前提下设计新加入子系统的控制律和自适应律, 使扩展后所有子系统都具有很好的跟踪性能. 这里主要利用神经网络的逼近功能以及Backstepping 技术来设计自适应律和控制律, 通过Lyapunov 理论证明在该控制器的作用下闭环系统的所有信号均是有界的, 并可使系统准确跟踪. 仿真结果验证了所提出方法的有效性.