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Instead of using the moving average (MA)-based noise model commonly used for adaptive digital control of linear discrete-time stochastic systems in the literature, an adjustable auto-regressive moving average (ARMA)-based noise model with estimated states is constructed for state-space self-tuning control of nonlinear continuous-time stochastic systems. By taking advantage of a digital redesign methodology, which converts a predesigned high-gain analog tracker\/observer into a practically implementable low-gain digital tracker\/observer, and by taking the non-negligible computation time delay and a relatively longer sampling period into consideration, a digitally redesigned predictive tracker\/observer has been newly developed in this paper for adaptive chaotic orbit tracking. The proposed method enables the development of a digitally implementable advanced control algorithm for nonlinear stochastic and chaotic hybrid systems. <\/jats:p>","DOI":"10.1142\/s0218127401002559","type":"journal-article","created":{"date-parts":[[2003,4,22]],"date-time":"2003-04-22T07:50:07Z","timestamp":1050997807000},"page":"1079-1113","source":"Crossref","is-referenced-by-count":24,"title":["STATE-SPACE SELF-TUNING CONTROL FOR NONLINEAR STOCHASTIC AND CHAOTIC HYBRID SYSTEMS"],"prefix":"10.1142","volume":"11","author":[{"given":"SHU-MEI","family":"GUO","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Houston, Houston, TX 77204-4005, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"LEANG-SAN","family":"SHIEH","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Houston, Houston, TX 77204-4005, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"CHING-FANG","family":"LIN","sequence":"additional","affiliation":[{"name":"American GNC Corporation, 888 E. 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