大系统分散控制中的信息结构

本文讨论控制系统中的信息结构,重点放在解决大系统分散控制过程中非经典信息结构的研究方法上,即所谓协群决策问题、结合传统的 LQG 同题,提出尚待深入研究的课题.

分散H_∞控制的LMI方法

提出了两种大系统分散Ｈ∞状态反馈控制的设计方法，首先描述了分散Ｈ∞状态反馈控制问题，然后提出了存在分散Ｈ∞状态反馈控制器的参数化定理和两种设计方法，即直接线性矩阵不等式（ＬＭＩ）方法和迭代ＬＭＩ（ＩＬＭＩ）方法，所获得的状态反馈阵具有块对角结构，且能满足闭环传递函数的Ｈ∞性能指标要求。

ROBUST SLIDING MODE DECENTRALIZED CONTROL FOR A CLASS OF NONLINEAR INTERCONNECTED LARGE-SCALE SYSTEM WITH NEURAL NETWORKS

A new decentralized robust control method is discussed for a class of nonlinear interconnected largescale system with unknown bounded disturbance and unknown nonlinear function term. A decentralized control law is proposed which combines the approximation method of neural network with sliding mode control. The decentralized controller consists of an equivalent controller and an adaptive sliding mode controller. The sliding mode controller is a robust controller used to reduce the track error of the control system. The neural networks are used to approximate the unknown nonlinear functions, meanwhile the approximation errors of the neural networks are applied to the weight value updated law to improve performance of the system. Finally, an example demonstrates the availability of the decentralized control method.

八十年代大系统理论展望(一)

本文的目的是探讨在大系统理论领域内有可能出现的发展,各种迹象表明,无论在概念上还是在实践上都将出现一些十分异乎寻常的发展。另一方面,本文还认为,除非科学家们对他们的研究领域准备抱有一种多少有些统观全局的观点,并探索出一些与他们过去的研究方向十分不同的道路,否则这些发展就不会实现。 几个领域的定量研究的相互会聚（mutual convergence）,很可能是这种新起点的潜力所在。这种会聚已经开始了并很可能在今后十年中加速发展。

STABILIZATION FOR A CLASS OF LARGE-SCALE STOCHASTIC NONLINEAR SYSTEMS WITH DECENTRALIZED CONTROLLER DESIGN

In this paper, a state feedback adaptive stabilization for a class of large-scale stochastic nonlinear systems is designed with Lyapunov and Backstepping method. In the systems there are uncertain terms, whose bounds are governed by a set of unknown parameters. The designed controllers would make the close-loop systems asymptotically stable and adaptive for the unknown parameters. As an application, a second order example is delivered to illustrate the approach.