Paper Title:
Adaptive Dynamic Surface Control for a Parametric Strict Feedback System with Actuator Failures
  Abstract

An adaptive dynamic surface control scheme for actuator failures compensation in a class of nonlinear system is presented. Radial basis function neural networks (RBF NNs) are incorporated into our controller design, for approximating the nonlinearities around the known nominal model. The RBF NNs compensate the system dynamics uncertainties and disturbance induced by actuator failures. The closed-loop signals of the system are proven to be uniformly ultimately bounded (UUB) by Lyapunov analysis. The output tracking error is bounded within a residual set which can be made small by appropriately choosing the controller parameters. We show the effectiveness of our approach by simulating the longitudinal dynamics of a twin otter aircraft with half portion of the elevator failing at unknown value and time instant.

  Info
Periodical
Chapter
Chapter 22: Metrology and Measurement
Edited by
Wu Fan
Pages
4381-4388
DOI
10.4028/www.scientific.net/AMM.110-116.4381
Citation
A. S. Kendrick, L. Yan, W. A. Butt, "Adaptive Dynamic Surface Control for a Parametric Strict Feedback System with Actuator Failures", Applied Mechanics and Materials, Vols. 110-116, pp. 4381-4388, 2012
Online since
October 2011
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