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Title: Neural-based direct adaptive control for a class of general nonlinear systems
Authors: Zhang, T.
Ge, S.S. 
Hang, C.C. 
Issue Date: 1997
Citation: Zhang, T.,Ge, S.S.,Hang, C.C. (1997). Neural-based direct adaptive control for a class of general nonlinear systems. International Journal of Systems Science 28 (10) : 1011-1020. ScholarBank@NUS Repository.
Abstract: A direct adaptive controller based on high-order neural networks (HONNs) is presented to solve the tracking control problem for a general class of unknown nonlinear systems. The plant is assumed to be a feedback linearizable and minimum-phase system. Firstly, an ideal implicit feedback linearization control (IFLC) is established using implicit function theory. Then a HONN is applied to construct this IFLC to realize approximate linearization. The proposed controller ensures that the closed-loop system is Lyapunov stable and that the tracking error converges to a small neighbourhood of the origin. The requirements of an off-line training phase and the persistant excitation condition are eliminated. Simulation results verify the effectiveness of the proposed controller and the theoretical discussion.
Source Title: International Journal of Systems Science
ISSN: 00207721
Appears in Collections:Staff Publications

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