This paper is concerned with a class of optimal output consensus control problems for discrete linear multiagent systems with the partially observable system state. Since the optimal control policy depends on the full system state which is not accessible for a partially observable system, traditionally, distributed observers are employed to recover the system state. However, in many situations, the accurate model of a real-world dynamical system might be difficult to obtain, which makes the observer design infeasible. Furthermore, the optimal consensus control policy cannot be analytically solved without system functions. To overcome these challenges, we propose a data-driven adaptive dynamic programming approach that does not require the complete system inner state. The key idea is to use the input and output sequence as an equivalent representation of the underlying state. Based on this representation, an adaptive dynamic programming algorithm is developed to generate the optimal control policy. For the implementation of this algorithm, we design a neural network-based actor-critic structure to approximate the local performance indices and the control polices. Two numerical simulations are used to demonstrate the effectiveness of our method.

中文翻译：

---

部分可观察的多主体系统的数据驱动的分布式输出共识控制

本文涉及具有部分可观测系统状态的离散线性多主体系统的一类最优输出共识控制问题。由于最佳控制策略取决于部分可观察系统无法访问的整个系统状态，因此传统上采用分布式观察器来恢复系统状态。然而，在许多情况下，可能难以获得真实世界动力系统的精确模型，这使得观察者的设计不可行。此外，没有系统功能就无法解析解决最佳共识控制策略。为了克服这些挑战，我们提出了一种数据驱动的自适应动态规划方法，该方法不需要完整的系统内部状态。关键思想是使用输入和输出序列作为基础状态的等效表示。基于此表示，开发了一种自适应动态规划算法以生成最佳控制策略。为了实现该算法，我们设计了一个基于神经网络的actor-critic结构来逼近局部性能指标和控制策略。两个数值模拟被用来证明我们方法的有效性。