A NARMAX model-based state-space self-tuning control for nonlinear stochastic hybrid systems |
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| Authors: | Jason Sheng-Hong Tsai Chu-Tong Wang Chi-Chieh Kuang Shu-Mei Guo Leang-San Shieh Chia-Wei Chen |
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| Affiliation: | 1. Department of Electrical Engineering, National Cheng-Kung University, Tainan 701, Taiwan, ROC;2. Department of Computer Science and Information Engineering, National Cheng-Kung University, Tainan 701, Taiwan, ROC;3. Department of Electrical and Computer Engineering, University of Houston, Houston, TX 77204-4005, USA;4. Department of Mechanical and Automation Engineering, Kao Yuan University, Kaohsiung County 821, Taiwan, ROC |
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| Abstract: | A novel state-space self-tuning control methodology for a nonlinear stochastic hybrid system with stochastic noise/disturbances is proposed in this paper. via the optimal linearization approach, an adjustable NARMAX-based noise model with estimated states can be constructed for the state-space self-tuning control in nonlinear continuous-time stochastic systems. Then, a corresponding adaptive digital control scheme is proposed for continuous-time multivariable nonlinear stochastic systems, which have unknown system parameters, measurement noise/external disturbances, and inaccessible system states. The proposed method enables the development of a digitally implementable advanced control algorithm for nonlinear stochastic hybrid systems. |
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| Keywords: | NARMAX model Nonlinear control systems State-space self-tuning control Optimal linearization |
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