SYSTEM IDENTIFICATION AND MODEL-BASED CONTROL OF A CHAOTIC SYSTEM

Abstract

In this paper the control of a hyper2chaotic system is considered to show the role of system identification techniques in developing a model for effective control of highly complex systems. An indirect adaptive control scheme is considered and it is shown that simple prediction models which cannot possibly represent the dynamics of the chaotic system lead to stable control. Furthermore, it is shown that higher dimensional prediction models which more closely represent the chaotic process dynamics lead to controlled systems with sparse and disjoint basins of attraction for the desired steady state solution. The use of highly nonlinear models also results in a complex pattern of convergence to the desired state.