Output recurrent wavelet neural network-based adaptive backstepping controller for a class of MIMO nonlinear non-affine uncertain systems

Ching Hung Lee*, Hua Hsiang Chang

*Corresponding author for this work

Research output: Contribution to journalArticle

6 Scopus citations

Abstract

In this paper, an adaptive backstepping control problem is proposed for a class of multiple-input-multiple-output nonlinear non-affine uncertain systems. An output recurrent wavelet neural network (ORWNN) is used to approximate the unknown nonlinear functions to develop the proposed adaptive backstepping controller. The proposed ORWNN combines the advantages of wavelet-based neural network, fuzzy neural network, and output feedback layer to achieve higher approximation accuracy and faster convergence. According to the estimation of ORWNN, the control scheme is designed by backstepping approach such that the system outputs follow the desired trajectories. Based on the Lyapunov approach, our approach guarantees that the system outputs converge to a small neighborhood of the references signals, that is, all signals of the closed-loop system are semi-globally uniformly ultimately bounded. Finally, simulation results including double pendulums system and two inverted pendulums on carts system are shown to demonstrate the performance and effectiveness of our approach.

Original languageEnglish
Pages (from-to)1035-1045
Number of pages11
JournalNeural Computing and Applications
Volume24
Issue number5
DOIs
StatePublished - Apr 2014

Keywords

  • Adaptive control
  • Backstepping
  • Nonlinear non-affine
  • Wavelet neural network

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