Data-Driven Optimized Output Regulation for Markov Jump Linear Systems and Its Application
研究了马尔可夫跳变线性系统的线性优化输出调节问题，提出基于模型和强化学习的方案，并在逆变器分布式发电系统中验证了性能。
The linear optimized output regulation problem (LOORP) for Markov jump linear systems (MJLSs) is investigated in this article. First, by improving previous methods for solving the regulator equations, a model-based scheme is proposed to address the LOORP of MJLSs. Subsequently, a reinforcement learning (RL)-based iteration scheme is developed, which enables the solution of the LOORP even when the system dynamics are partially unknown and the initial control gains are unstable. Finally, an LCL-coupled inverter-based distributed generation system is provided to demonstrate the performance of the proposed RL-based scheme.