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

This research proposes a framework that combines reinforcement learning (RL, a machine learning technique where a system learns by trial and error and receiving rewards), control barrier functions (CBFs, mathematical tools that keep a system operating within safe limits), and active fault-tolerant control (AFTC, a method that detects and responds to component failures in real time) to help multi-agent systems (groups of coordinated AI agents or robots) track targets accurately even when their actuators (motors or mechanisms that create movement) fail. The approach ensures safety by maintaining a predefined safe operating zone while using a computationally efficient method suitable for large-scale deployments.