Adaptive Dynamic Programming and Data-Driven Cooperative Optimal Output Regulation with Adaptive Observers
Adaptive Dynamic Programming and Data-Driven Cooperative Optimal Output Regulation with Adaptive Observers
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DOI：
10.1109/cdc51059.2022.9993124
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发表时间：
2022-09
期刊：
影响因子：
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通讯作者：
Omar Qasem;K. Jebari;Weinan Gao
中科院分区：
文献类型：
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作者：
Omar Qasem;K. Jebari;Weinan Gao
针对连续时间线性多智能体系统，提出了一种基于自适应动态规划（adaptive dynamic programming，ADP）的自适应最优控制策略，以实现系统的协作最优输出调节.所提出的方法是不同的ADP和合作的输出调节在这个意义上说，外系统动力学的知识是不需要在设计的外状态观测器的那些代理没有直接访问外系统的现有文献。此外，最优控制策略获得没有任何代理的建模信息的先验知识，同时实现合作的输出调节。相反，我们使用状态/输入信息沿着的轨迹的底层动力系统和估计的外状态学习的最优控制策略。仿真结果表明，该算法的有效性，外系统矩阵和外状态的估计误差以及跟踪误差均在最优意义下收敛到零，解决了协作最优输出调节问题。
In this paper, a novel adaptive optimal control strategy is proposed to achieve the cooperative optimal out-put regulation of continuous-time linear multi-agent systems based on adaptive dynamic programming (ADP). The proposed method is different from those in the existing literature of ADP and cooperative output regulation in the sense that the knowledge of the exosystem dynamics is not required in the design of the exostate observers for those agents with no direct access to the exosystem. Moreover, an optimal control policy is obtained without the prior knowledge of the modeling information of any agent while achieving the cooperative output regulation. Instead, we use the state/input information along the trajectories of the underlying dynamical systems and the estimated exostates to learn the optimal control policy. Simulation results show the efficacy of the proposed algorithm, where both estimation errors of exosystem matrix and exostates, and the tracking errors converge to zero in an optimal sense, which solves the cooperative optimal output regulation problem.