Identification and Adaptive Control of Dynamic Systems Using Self-Organized Distributed Networks

Abstract

Abstract An adaptive control technique, using system identification based on Self-Organized Distributed Networks (SODNs), is presented for a class of discrete nonlinear dynamic systems having unknown dynamics. The SODN belongs to the category of distributed local learning networks and is composed of two main networks called the learning network and the distribution network. The learning network consists of subnets each responsible for a subproblem. The distribution network is responsible for input space decomposition. The learning of the SODN is fast and precise because of the local learning mechanism. In this paper, methods for identification and indirect adaptive control of nonlinear systems using the SODN are presented. Through extensive simulation, the SODN is shown to be effective both for identification and adaptive control of nonlinear dynamic systems.