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Indirect Adaptive Neuro-Sliding Mode Control for SISO Nonlinear Systems with State Observer

Slim Frikha*, Mohamed Djemel**, N. Derbel***

*,**Research Unit on intelligent control Design and Optimisation of Complex Systems(ICOS),University of Sfax,ENIS,Tunisia.

***Head of Research Unit on intelligent control Design and Optimisation of Complex Systems(ICOS),University of Sfax,ENIS,Tunisia.

Periodicity:May - July'2007
DOI : https://doi.org/10.26634/jfet.2.4.808

Abstract

This paper proposes an observer based indirect adaptive sliding mode controller with neural networks for a certain class of unknown nonlinear dynamic systems. For these systems not all the state variables are available for measurements. To design the proposed controller, we first construct neural network models to describe system nonlinear dynamics. Then, an observer is employed to estimate the tracking error vector, and a neuro-sliding mode controller is developed to achieve the tracking performances. Adaptive laws are proposed to adjust parameters of neural models. The stability of the overall control system is analyzed based on the Lyapunov method. Simulation results illustrate the design procedures and show the tracking performance of the proposed controller in the presence of external disturbances.

Keywords

Adaptive control, neural networks, sliding mode control, observers, stability.

How to Cite this Article?

Slim Frikha, Mohamed Djemel and N. Derbel (2007). Indirect Adaptive Neuro-Sliding Mode Control for SISO Nonlinear Systems with State Observer. i-manager’s Journal on Future Engineering and Technology, 2(4), 68-74. https://doi.org/10.26634/jfet.2.4.808

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