It is demonstrated that neural networks can be used effectively for the identification and control of nonlinear dynamical systems. The emphasis is on models for both identification and control. Static and dynamic backpropagation methods for the adjustment of parameters are discussed. In the models that are introduced, multilayer and recurrent networks are interconnected in novel configurations, and hence there is a real need to study them in a unified fashion. Simulation results reveal that the identification and adaptive control schemes suggested are practically feasible. Basic concepts and definitions are introduced throughout, and theoretical questions that have to be addressed are also described. >

Identification and control of dynamical systems using neural networks

Networked control systems (NCSs) are spatially distributed systems for which the communication between sensors, actuators, and controllers is supported by a shared communication network. We review several recent results on estimation, analysis, and controller synthesis for NCSs. The results surveyed address channel limitations in terms of packet-rates, sampling, network delay, and packet dropouts. The results are presented in a tutorial fashion, comparing alternative methodologies

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A Survey of Recent Results in Networked Control Systems

By a switched system, we mean a hybrid dynamical system consisting of a family of continuous-time subsystems and a rule that orchestrates the switching between them. The article surveys developments in three basic problems regarding stability and design of switched systems. These problems are: stability for arbitrary switching sequences, stability for certain useful classes of switching sequences, and construction of stabilizing switching sequences. We also provide motivation for studying these problems by discussing how they arise in connection with various questions of interest in control theory and applications.

Basic problems in stability and design of switched systems

关于Semigroups of Linear Operators and Applications to Partial Differential Equations的两个注解

During the past several years, there have been increasing research activities in the field of stability analysis and switching stabilization for switched systems. This paper aims to briefly survey recent results in this field. First, the stability analysis for switched systems is reviewed. We focus on the stability analysis for switched linear systems under arbitrary switching, and we highlight necessary and sufficient conditions for asymptotic stability. After a brief review of the stability analysis under restricted switching and the multiple Lyapunov function theory, the switching stabilization problem is studied, and a variety of switching stabilization methods found in the literature are outlined. Then the switching stabilizability problem is investigated, that is under what condition it is possible to stabilize a switched system by properly designing switching control laws. Note that the switching stabilizability problem has been one of the most elusive problems in the switched systems literature. A necessary and sufficient condition for asymptotic stabilizability of switched linear systems is described here.

Stability and Stabilizability of Switched Linear Systems: A Survey of Recent Results

Lyapunov Stability of Dynamical Systems determinined by Differential Inequalities with Applications to Robust Stability 1

In the present paper we establish new sufficient conditions for the global asymptotic stability of uncertain linear systems described by ordinary differential equations under saturation constraints We consider systems operating on the unit hypercube in Rn (where all states are subject to saturation constraints) and systems with partial state saturation constraints (where only some of the states are subject to saturation constraints) Systems of the type which we consider are widely used in several areas of applications, including control systems, signal processing, and artificial neural networks We demonstrate the usefulness of our results by means of two specific examples

Stability analysis of uncertain systems with saturation constraints

A comparison theory for the stability analysis of general hybrid systems

An Introduction to State-Space and Input–Output Descriptions of Systems

This paper presents some developments in the analysis and design of a class of feedback neural networks with sparse interconnecting structure. The analysis results presented make it possible to determine whether a given vector is a stable memory of a neural network and to what extent implementation errors are permissible. The design methods presented allow the synthesis of neural networks with predetermined sparse interconnecting structures with or without symmetry constraints on the interconnection weights. An example is included to demonstrate the applicability of the methodology advanced herein.

Anthony N. Michel

Papers

Lyapunov Stability of Dynamical Systems determinined by Differential Inequalities with Applications to Robust Stability 1

Stability analysis of uncertain systems with saturation constraints

A comparison theory for the stability analysis of general hybrid systems

An Introduction to State-Space and Input–Output Descriptions of Systems

Theory and applications of sparsely interconnected feedback neural networks