This paper proposes an optimal output synchronization control method for heterogeneous multi-agent systems (HMASs) under a performance-dependent switching topology and DoS attacks. First, local and global switched performance index functions (SPIF) and SPIF-dependent topology switching law are proposed, respectively, thus, the control performance and topology quality can be quantitatively expressed. Second, an adaptive dynamic programming (ADP) algorithm with mode switching is proposed, aimed at dealing with the difficult Hamilton–Jacobi–Bellman equation, as well as the analytical complexity caused by the switching dynamics. The convergence of the switched ADP algorithm is proven to ensure its correct implementation. Then, for different topologies, multi-mode Actor–Critic neural networks (NNs) are built for each agent to calculate optimized control policies and SPIF, respectively. Furthermore, an NN-based state compensation mechanism is designed to expand the applicability of the designed switched ADP algorithm when the leader’s output transmission is unreliable. Finally, the results of numerical examples confirm that the proposed method is feasible.

中文翻译：

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多代理系统的最佳同步：依赖于性能的交换拓扑

本文提出了一种在性能相关的交换拓扑和 DoS 攻击下异构多智能体系统 (HMAS) 的最优输出同步控制方法。首先，分别提出了局部和全局切换性能指数函数（SPIF）和依赖于SPIF的拓扑切换律，从而可以定量地表达控制性能和拓扑质量。其次，提出了一种具有模式切换的自适应动态规划（ADP）算法，旨在处理困难的 Hamilton-Jacobi-Bellman 方程以及切换动态引起的分析复杂性。证明了交换ADP算法的收敛性，保证了其正确实现。然后，针对不同的拓扑，为每个智能体构建多模式 Actor-Critic 神经网络（NN），分别计算优化的控制策略和 SPIF。此外，还设计了基于神经网络的状态补偿机制，以扩展所设计的切换ADP算法在领导者输出传输不可靠时的适用性。最后，数值算例的结果证实了该方法的可行性。