Adaptive Optimal Control of Linear Periodic Systems: An Off-Policy Value Iteration Approach

• 成果类型: Article
• 署名作者: Pang, Bo; Jiang, Zhong-Ping
• 署名单位: New York University; New York University Tandon School of Engineering
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2020.2987313
• 发表日期: 2021
• 页码: 888-894
• 关键词: Adaptive dynamic programming; linear periodic systems; optimal control; value iteration

摘要

This article studies the infinite-horizon adaptive optimal control of continuous-time linear periodic (CTLP) systems. A novel value iteration (VI) based off-policy adaptive dynamic programming (ADP) algorithm is proposed for a general class of CTLP systems, so that approximate optimal solutions can be obtained directly from the collected data, without the exact knowledge of system dynamics. Under mild conditions, the proofs on uniform convergence of the proposed algorithm to the optimal solutions are given for both the model-based and model-free cases. The VI-based ADP algorithm is able to find suboptimal controllers without assuming the knowledge of an initial stabilizing controller. Application to the optimal control of a triple inverted pendulum subjected to a periodically varying load demonstrates the feasibility and effectiveness of the proposed method.