An Online Model-Following Projection Mechanism Using Reinforcement Learning

• 成果类型: Article
• 署名作者: Abouheaf, Mohammed I.; Hashim, Hashim A.; Mayyas, Mohammad A.; Vamvoudakis, Kyriakos G.
• 署名单位: University System of Ohio; Bowling Green State University; Carleton University; University System of Georgia; Georgia Institute of Technology
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2023.3243165
• 发表日期: 2023
• 页码: 6959-6966
• 关键词: Asymptotic stabilit; model-free adaptive learning; model reference adaptive systems; Neural Network; Reinforcement Learning

摘要

In this article, 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.