Event-Triggered Practical Fixed-Time Integral Reinforcement Learning for Unknown Nonlinear Systems
This paper develops an event-triggered fixed-time integral reinforcement learning framework for optimal control of unknown nonlinear systems. An integral data-driven identifier is first used to reconstruct the unknown dynamics, after which an inverse-optimal formulation is employed to construct a fixed-time running cos...