Safety-Critical Model-Free Adaptive Iterative Learning Control for Multi-Agent Consensus Using Control Barrier Functions

Aiming at the problem of safety of unknown multi-agent systems in the process of executing repetitive tasks, an novel iterative learning control barrier functions is proposed in this brief. A data-driven consensus controller is designed for multi-agent systems with repetitive tasks and uncertain dynamic model parameters. In order to ensure the output safety of agents in each iteration, a safety-critical control in the iteration domain is proposed. Namely, a novel iterative learning control barrier function is proposed, combined with the proposed consistent control law, a quadratic programming is constructed for the control output. When the expected output conflicts with the safety boundary, the controller can prioritize the safety of the agents. Finally, a multi-agent system with repetitive characteristics is designed to verify the theoretical results.