Online Safety-Embedded Critic Learning for Uncertain Systems With Self-Triggered Mechanism

Summary

This paper proposes a learning-based optimal control framework for safety-critical systems that combines a primal–dual value function built on a Lyapunov-compensated control barrier function (LCBF) with a self-triggered mechanism that schedules controller updates. An online safety-embedded critic learning method with concurrent learning approximates the controller by evaluating Bellman errors under the safety-certified controller. Simulation results are reported to show safety assurance, control performance, and reduced computational load.