论文标题
自适应动态编程和数据驱动的合作最佳输出调节，并具有自适应观察者
Adaptive Dynamic Programming and Data-Driven Cooperative Optimal Output Regulation with Adaptive Observers
论文作者
论文摘要
在本文中，提出了一种新型的自适应最佳控制策略，以实现基于自适应动态编程（ADP）的连续时间线性多代理系统的合作最佳输出调节。所提出的方法与ADP现有文献和合作输出调节中的方法不同，因为对于那些无法直接访问外部系统的代理人，在外部观察者的设计中不需要对外部系统动力学的知识。此外，在实现合作产出调节的同时，在没有任何代理商的建模信息的情况下获得了最佳控制策略。取而代之的是，我们使用沿基本动力学系统和估计的Exostates的轨迹的状态/输入信息来学习最佳控制策略。仿真结果显示了所提出的算法的功效，其中外部系统矩阵和外部的估计误差以及跟踪误差以最佳意义收敛到零，从而解决了合作的最佳输出调控问题。
In this paper, a novel adaptive optimal control strategy is proposed to achieve the cooperative optimal output 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.
