Event-Triggered Practical Fixed-Time Integral Reinforcement Learning for Unknown Nonlinear Systems
The research develops an event-triggered framework for integral reinforcement learning to control unknown nonlinear systems. It combines system identification with an inverse-optimal formulation to achieve practical fixed-time stability while minimizing communication. The method successfully excludes Zeno behavior, ensuring robust performance in complex, unknown environments.