Adaptive Dynamic Surface Control for a Parametric Strict Feedback System with Actuator Failures

Abstract:

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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:

Edited by:

Wu Fan

Pages:

4381-4388

DOI:

10.4028/www.scientific.net/AMM.110-116.4381

Citation:

A. S. Kendrick et al., "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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$35.00

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