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

^** Email: shtsai{at}mail.ncku.edu.tw In this paper, subject to acceptable closed-loop performance,an effective lower-order tuner for a stochastic chaotic hybridsystem is designed using the observer/Kalman filter identification(OKID) method, in which the system state in a general coordinateform is transformed to one in an observer form. The OKID methodis a time-domain technique that identifies a discrete input–outputmap by using known input–output sampled data in the generalcoordinate form, through an extension of the eigensystem realizationalgorithm. Moreover, it provides a lower-order realization ofthe tracker, with computationally effective initialization,for on-line "auto-regressive moving average process with exogenousmodel" -based identification and a lower-order state-space self-tuningcontrol technique. Finally, the chaotic Chen's system is usedas an illustrative example to demonstrate the effectivenessof the proposed methodology.