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Communication Dans Un Congrès Année : 2020

Descriptor Recurrent Neural Network Model and L2 gain Control Design for Systems with Dry Friction

Résumé

This paper presents a control design method for mechanical systems involving the Coulomb and Stribeck dry friction effects. The design method is based on a continuous-time recurrent neural network (RNN) descriptor model. The latter has the particularity of including a single nonlinearity verifying the well-known cone bounded condition. Hence, a nonlinear static feedback is proposed guaranteeing an efficient friction compensation and optimizing the ℒ 2 - gain of the closed-loop. The design procedure is recast as an optimization problem under LMI constraints that can be solved efficiently. The relevance of the proposed approach is confirmed through a didactic example.

Domaines

Automatique
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Dates et versions

hal-02971958 , version 1 (20-10-2020)

Identifiants

Citer

Mohamed Yagoubi, Redouane Chaibi, Marc Lunel. Descriptor Recurrent Neural Network Model and L2 gain Control Design for Systems with Dry Friction. ICSTCC 2020 : 24th International Conference on Systems Theory, Control and Computing, Oct 2020, Sinaia, Romania. ⟨10.1109/ICSTCC50638.2020.9259692⟩. ⟨hal-02971958⟩
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