Data-Driven Optimal Synchronization Control for Leader-Follower Multiagent Systems
针对带有加性扰动和未知系统矩阵的领导者-跟随者多智能体系统，提出了两种数据驱动的最优同步控制算法，利用输出同步误差或输入数据进行在线学习，在保证同步的同时最小化误差并抑制扰动。
In this article, we develop data-driven optimal synchronization control architectures for leader-follower multiagent systems with additive disturbances and unknown system matrices. To minimize output synchronization error, algebraic Riccati equations (AREs) are derived, and unique feedback gains are determined by policy iteration. On that basis, two data-driven optimal synchronization control algorithms are developed without relying on the dynamics of the system, which guarantee output synchronization while minimizing synchronization errors and rejecting disturbances. The first algorithm uses the output synchronization error data to perform online data-driven learning (DDL), while the second algorithm uses the input data to perform DDL, where both data sample requirements are transformed into rank conditions. We have presented rigorous theoretical analyses of our proposed algorithms, which demonstrate that if an initial control protocol can make the system achieve output synchronization under mild conditions, our proposed two algorithms can take advantage of the data from reaching synchronization to optimize the closed-loop performance. Finally, a numerical example is provided to emphasize the effectiveness of our methods.