Reinforcement Learning-Based Active Fault-Tolerant Control of Multi-Agent Systems

Summary

This article proposes an integrated framework for optimal tracking control of multi-agent systems with actuator faults. It combines reinforcement learning for the tracking controller, active fault-tolerant control that reconfigures the control law from real-time fault diagnostics, and a control barrier function safety mechanism. The authors report that a low-complexity approach avoids conventional quadratic programming for safety constraints, and that simulations show stable consensus tracking and preserved safety under faults.