Predefined-Time Integral Reinforcement Learning for Unknown Nonlinear Systems via Inverse-Optimal Design
This framework develops a method for controlling unknown nonlinear systems using integral reinforcement learning combined with inverse-optimal design. It first identifies the system's unknown dynamics and then uses predefined-time comparison results to set a guaranteed convergence deadline. The resulting control policy is learned directly from integral data, achieving practical predefined-time stability without needing exact system knowledge.