Abstract In this paper, a novel control scheme is developed to solve an optimal containment control problem of unknown continuous-time multi-agent systems. Different from traditional adaptive dynamic programming (ADP) algorithms, this paper proposes an internal reinforcement ADP algorithm (IR-ADP), in which the internal reinforcement signals are added in order to facilitate the learning process. Then a distributed containment control law is designed for each agent with the internal reinforcement signal. The convergence of this IR-ADP algorithm and the stability of the closed-loop multi-agent system are analyzed theoretically. For the implementation of the optimal controllers, three neural networks (NNs), namely internal reinforcement NNs, critic NNs and actor NNs, are utilized to approximate the internal reinforcement signals, the performance indices and optimal control laws, respectively. Finally, some simulation results are provided to demonstrate the effectiveness of the proposed algorithm.

中文翻译：

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未知连续时间多智能体系统最优包含控制的内强化自适应动态规划

摘要 在本文中，开发了一种新的控制方案来解决未知连续时间多智能体系统的最优遏制控制问题。与传统的自适应动态规划（ADP）算法不同，本文提出了一种内部强化ADP算法（IR-ADP），其中加入了内部强化信号以促进学习过程。然后为每个具有内部增强信号的代理设计分布式遏制控制律。从理论上分析了该IR-ADP算法的收敛性和闭环多智能体系统的稳定性。为了实现最优控制器，三个神经网络 (NN)，即内部强化 NN、评论家 NN 和演员 NN，用于近似内部强化信号，分别为性能指标和最优控制律。最后，提供了一些仿真结果来证明所提出算法的有效性。