An Online Model-Following Projection Mechanism Using Reinforcement Learning
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
Cite
@article{arxiv.2302.02493,
title = {An Online Model-Following Projection Mechanism Using Reinforcement Learning},
author = {Mohammed I. Abouheaf and Hashim A. Hashim and Mohammad A. Mayyas and Kyriakos G. Vamvoudakis},
journal= {arXiv preprint arXiv:2302.02493},
year = {2023}
}
Comments
IEEE Transactions on Automatic Control