Adaptive backstepping output feedback control for SISO nonlinear system using fuzzy neural networks |
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Authors: | Shao-Cheng Tong Yong-Ming Li |
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Affiliation: | (1) Department of Basic Mathematics, Liaoning University of Technology, Jinzhou, 121001, PRC |
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Abstract: | In this paper, a new fuzzy-neural adaptive control approach is developed for a class of single-input and single-output (SISO) nonlinear systems with unmeasured states. Using fuzzy neural networks to approximate the unknown nonlinear functions, a fuzzy-neural adaptive observer is introduced for state estimation as well as system identification. Under the framework of the backstepping design, fuzzy-neural adaptive output feedback control is constructed rccursively. It is proven that the proposed fuzzy adaptive control approach guarantees the global boundedness property for all the signals, driving the tracking error to a small neighbordhood of the origin. Simulation example is included to illustrate the effectiveness of the proposed approach. |
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Keywords: | Nonlinear systems backstepping control adaptive fuzzy neural networks control state observer output feedback control |
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