Data-Driven Optimal Output Regulation for Continuous-Time Linear Systems via Internal Model Principle

• 成果类型: Article
• 署名作者: Lin, Liquan; Huang, Jie
• 署名单位: Chinese University of Hong Kong
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2025.3535281
• 发表日期: 2025
• 页码: 4202-4208
• 关键词: Linear systems; regulation; mathematical models; Heuristic algorithms; vectors; Symmetric matrices; computational efficiency; State feedback; Feedforward systems; Approximation algorithms; Data-driven control; internal model principle; output regulation; reinforcement learning (RL); value iteration (VI)

摘要

The data-driven techniques have been developed to deal with the output regulation problem of unknown linear systems by various approaches. In this article, we first extend an existing result from single-input single-output linear systems to multi-input multi-output linear systems. Then, by separating the dynamics used in the learning phase and the control phase, we further propose an improved algorithm that significantly reduces the computational cost and weaken the solvability conditions for the first algorithm. A numerical example is used to illustrate the advantages of the improved algorithm.