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|Title:||Neural-based direct adaptive control for a class of general nonlinear systems||Authors:||Zhang, T.
|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||URI:||http://scholarbank.nus.edu.sg/handle/10635/80786||ISSN:||00207721|
|Appears in Collections:||Staff Publications|
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