Neural network-based robust adaptive control of nonlinear systems with unmodeled dynamics

Dan Wang, Jialiang Huang, Weiyao Lan and Xiaoqiang Li

Mathematics and Computers in Simulation (MATCOM), 2009, vol. 79, issue 5, 1745-1753

Abstract: A neural network-based robust adaptive control design scheme is developed for a class of nonlinear systems represented by input–output models with an unknown nonlinear function and unmodeled dynamics. By on-line approximating the unknown nonlinear functions and unmodeled dynamics by radial basis function (RBF) networks, the proposed approach does not require the unknown parameters to satisfy the linear dependence condition. It is proved that with the proposed control law, the closed-loop system is stable and the tracking error converges to zero in the presence of unmodeled dynamics and unknown nonlinearity. A simulation example is presented to demonstrate the method.

Keywords: Adaptive control; Neural networks; Nonlinear control; Robustness; Unmodeled dynamics (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:79:y:2009:i:5:p:1745-1753

DOI: 10.1016/j.matcom.2008.09.002

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