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Exponential stability criteria for neural network based control of nonlinear systems

Abstract : This paper investigates the problem of exponential stability of neural network controlled sampled-data systems. The stability of the closed loop system is formulated using Lyapunov-Krasovskii approach. Using a time-dependent Lyapunov function, novel conditions are proposed to guarantee the exponential stability of a continuous aperiodically sampled nonlinear system. The stability conditions are derived in terms of solvable Linear Matrix Inequalities. Numerical results are provided to illustrate the effectiveness of the proposed approach.
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Submitted on : Wednesday, October 27, 2021 - 3:47:40 PM
Last modification on : Wednesday, November 3, 2021 - 3:59:39 AM

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Ayush Kumar Jain, Christophe Fiter, Denis Berdjag, Philippe Polet. Exponential stability criteria for neural network based control of nonlinear systems. 2020 American Control Conference (ACC), Jul 2020, Denver, United States. pp.1631-1636, ⟨10.23919/ACC45564.2020.9147848⟩. ⟨hal-03406028⟩

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