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

This article presents a method for teaching AI systems to control complex machinery safely and efficiently by combining optimal control (finding the best way to manage a system), safety constraints (hard limits that must never be broken), and a self-triggered mechanism (a system that only updates calculations when necessary). The approach uses a safety-filter structure to balance the competing goals of optimal performance and guaranteed safety, while reducing the computing power needed.