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arXiv · 2302.02493

An Online Model-Following Projection Mechanism Using Reinforcement Learning

Mohammed I. Abouheaf·Hashim A. Hashim·Mohammad A. Mayyas·Kyriakos G. Vamvoudakis
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Abstract

In this paper, we propose a model-free adaptive learning solution for a model-following control problem. This approach employs policy iteration, to find an optimal adaptive control solution. It utilizes a moving finite-horizon of model-following error measurements. In addition, the control strategy is designed by using a projection mechanism that employs Lagrange dynamics. It allows for real-time tuning of derived actor-critic structures to find the optimal model-following strategy and sustain optimized adaptation performance. Finally, the efficacy of the proposed framework is emphasized through a comparison with sliding mode and high-order model-free adaptive control approaches. Keywords: Model Reference Adaptive Systems, Reinforcement Learning, adaptive critics, control system, stochastic, nonlinear system

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Mohammed I. Abouheaf, Hashim A. Hashim, Mohammad A. Mayyas, Kyriakos G. Vamvoudakis. 2023-02-05. An Online Model-Following Projection Mechanism Using Reinforcement Learning. https://doi.org/10.1109/tac.2023.3243165

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