Please use this identifier to cite or link to this item: https://doi.org/10.1109/TNN.2010.2042611
Title: Robust adaptive neural network control for a class of uncertain MIMO nonlinear systems with input nonlinearities
Authors: Chen, M.
Ge, S.S. 
How, B.V.E. 
Keywords: Backstepping control
Input nonlinearity
Neural networks (NNs)
Nonlinear systems
Variable structure control (VSC)
Issue Date: May-2010
Source: Chen, M., Ge, S.S., How, B.V.E. (2010-05). Robust adaptive neural network control for a class of uncertain MIMO nonlinear systems with input nonlinearities. IEEE Transactions on Neural Networks 21 (5) : 796-812. ScholarBank@NUS Repository. https://doi.org/10.1109/TNN.2010.2042611
Abstract: In this paper, robust adaptive neural network (NN) control is investigated for a general class of uncertain multiple-inputmultiple-output (MIMO) nonlinear systems with unknown control coefficient matrices and input nonlinearities. For nonsymmetric input nonlinearities of saturation and deadzone, variable structure control (VSC) in combination with backstepping and Lyapunov synthesis is proposed for adaptive NN control design with guaranteed stability. In the proposed adaptive NN control, the usual assumption on nonsingularity of NN approximation for unknown control coefficient matrices and boundary assumption between NN approximation error and control input have been eliminated. Command filters are presented to implement physical constraints on the virtual control laws, then the tedious analytic computations of time derivatives of virtual control laws are canceled. It is proved that the proposed robust backstepping control is able to guarantee semiglobal uniform ultimate boundedness of all signals in the closed-loop system. Finally, simulation results are presented to illustrate the effectiveness of the proposed adaptive NN control. © 2010 IEEE.
Source Title: IEEE Transactions on Neural Networks
URI: http://scholarbank.nus.edu.sg/handle/10635/50732
ISSN: 10459227
DOI: 10.1109/TNN.2010.2042611
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