Neural Adaptive Dynamic Event-Triggered Containment Control for Uncertain Multi-Agent Systems Under Markovian Switching Dynamics

In this paper, we propose the containment control problem for multi-agent systems with Markovian switching dynamics by proposing a novel adaptive dynamic event-triggered sliding mode control scheme based on radial basis function neural networks. First, the unknown nonlinear dynamics of the system were approximated by using radial basis function neural networks. The dynamic event-triggered control scheme designed in the framework of sliding mode control operated at specific event sampling moments, thereby reducing computational and communication burdens. The containment control was achieved through a synergistic approach integrating dynamic event-triggered control with neural network-based adaptive control in a stochastic switching system. Moreover, we proved that Zeno behavior was effectively avoided. The proposed distributed containment control technique was validated through simulations, demonstrating its effectiveness and superiority.