Online reinforcement learning control via discontinuous gradient

Author:

Arellano‐Muro Carlos A.12,Castillo‐Toledo Bernardino1,Di Gennaro Stefano34ORCID,Loukianov Alexander G.1

Affiliation:

1. Automatic Control CINVESTAV‐IPN Campus Guadalajara Zapopan Jalisco Mexico

2. Industrial Engineering Department CUCEI–Universidad de Guadalajara Guadalajara Jalisco Mexico

3. Department of Information Engineering, Computer Science and Mathematics University of L'Aquila L'Aquila Italy

4. Center of Excellence DEWS University of L'Aquila L'Aquila Italy

Abstract

SummaryThis work proposes a reinforcement learning control scheme for systems affected by persistent external perturbations. This scheme relies on and high‐order sliding mode control techniques combined to estimate the parameters with a certain degree of precision and simultaneously attenuate persistent and state‐dependent perturbations. The proposed solution is a novel design technique based on the minimization method via Discontinuous Gradient. The stability of the proposed scheme is proved via the Lyapunov approach.

Funder

Consejo Nacional de Ciencia y Tecnología

Publisher

Wiley

Reference37 articles.

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3. Universal single–input–single–output (SISO) sliding–mode controllers with finite time convergence;Levant A;IEEE Trans Automat Contr,2001

4. A multivariable super-twisting sliding mode approach

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