Lower-order state-space self-tuning control for a stochastic chaotic hybrid system

Abstract

In this paper, subject to acceptable closed-loop performance, an effective lower-order tuner for a stochastic chaotic hybrid system is designed using the observer/Kalman filter identification (OKID) method, in which the system state in a general coordinate form is transformed to one in an observer form. The OKID method is a time-domain technique that identifies a discrete input–output map by using known input–output sampled data in the general coordinate form, through an extension of the eigensystem realization algorithm. Moreover, it provides a lower-order realization of the tracker, with computationally effective initialization, for on-line “auto-regressive moving average process with exogenous model” -based identification and a lower-order state-space self-tuning control technique. Finally, the chaotic Chen's system is used as an illustrative example to demonstrate the effectiveness of the proposed methodology.