RL-Driven Self-Triggered Optimal Stabilization for Unknown Nonlinear Networked Industrial Systems: An Adaptive Fuzzy Identification Design
This article addresses the optimal stabilization of unknown nonlinear networked industrial systems (NISs) with limited communication resources, proposing an innovative self-triggered approximate optimal control framework fused with generalized fuzzy hyperbolic model (GFHM) and reinforcement learning (RL) gradient desce...