Please use this identifier to cite or link to this item: https://doi.org/10.1109/TSMCB.2004.826827
Title: Adaptive neural network control for a class of MIMO nonlinear systems with disturbances in discrete-time
Authors: Ge, S.S. 
Zhang, J.
Lee, T.H. 
Issue Date: Aug-2004
Citation: Ge, S.S., Zhang, J., Lee, T.H. (2004-08). Adaptive neural network control for a class of MIMO nonlinear systems with disturbances in discrete-time. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 34 (4) : 1630-1645. ScholarBank@NUS Repository. https://doi.org/10.1109/TSMCB.2004.826827
Abstract: In this paper, adaptive neural network (NN) control is investigated for a class of multiinput and multioutput (MIMO) nonlinear systems with unknown bounded disturbances in discrete-time domain. The MIMO system under study consists of several subsystems with each subsystem in strict feedback form. The inputs of the MIMO system are in triangular form. First, through a coordinate transformation, the MIMO system is transformed into a sequential decrease cascade form (SDCF). Then, by using high-order neural networks (HONN) as emulators of the desired controls, an effective neural network control scheme with adaptation laws is developed. Through embedded backstepping, stability of the closed-loop system is proved based on Lyapunov synthesis. The output tracking errors are guaranteed to converge to a residue whose size is adjustable. Simulation results show the effectiveness of the proposed control scheme. © 2004 IEEE.
Source Title: IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
URI: http://scholarbank.nus.edu.sg/handle/10635/50860
ISSN: 10834419
DOI: 10.1109/TSMCB.2004.826827
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