Iterative learning fuzzy control with optimal gains for a class of nonlinear systems

Abstract : This paper proposes a novel P-type iterative learning fuzzy control with optimal gains for a class of Multi Input Multi Output (MIMO) nonlinear systems. The control design is very simple, in the sense that we use just a proportional learning action. Another advantage of this proposed controller is that, the global Lipschitz condition is not required for nonlinear systems. Thus, to approximate the unknown nonlinear function, we use a fuzzy logic term. In addition, the swarm optimization algorithm is used to design the optimum iterative learning fuzzy control (ILFC), in the sense that the tracking errors converge at the fastest rate. To prove the asymptotic stability of the closed loop system over the whole finite time, Lyapunov theory is used. Finally and to illustrate the effectiveness of the proposed control scheme, simulation results are presented.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-02354782
Contributor : Didier Maquin <>
Submitted on : Thursday, November 7, 2019 - 9:56:52 PM
Last modification on : Friday, November 8, 2019 - 1:17:53 AM

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Tarek Bensidhoum, Farah Bouakrif, Michel Zasadzinski. Iterative learning fuzzy control with optimal gains for a class of nonlinear systems. 6th International Conference on Control, Decision and Information Technologies, CoDIT 2019, Apr 2019, Paris, France. ⟨10.1109/CoDIT.2019.8820343⟩. ⟨hal-02354782⟩

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