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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 fi
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