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Lower-order state-space self-tuning control for a stochastic chaotic hybrid system
Authors:Chien, Tseng-Hsu   Tsai, Jason Sheng Hong   Guo, Shu-Mei   Chen, Guanrong
Affiliation:1 Control System Laboratory, Department of Electrical Engineering, National Cheng-Kung University, Tainan 701, Taiwan, Republic of China, 2 Department of Computer Science and Information Engineering, National Cheng-Kung University, Tainan 701, Taiwan, Republic of China, 3 Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong SAR, People's Republic of China
Abstract:** 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.
Keywords:self-tuning control   stochastic system   chaotic system   orbit tracker   Markov parameters.
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