RL-Driven Self-Triggered Optimal Stabilization for Unknown Nonlinear Networked Industrial Systems: An Adaptive Fuzzy Identification Design

Abstract

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 descent. To handle unknown dynamics without prior model information, a GFHM-based state identifier is constructed, leveraging its universal approximation, flexible structure, and fewer parameters to adaptively reconstruct nonlinear dynamics. A self-triggered mechanism predicts the next control update instant via software, eliminating continuous hardware monitoring and reducing online computing cost, network data transmission overhead, and actuator energy usage. Within the RL framework, a critic neural network (NN) is established, with weights tuned via gradient descent to approximate the Hamilton-Jacobi-Bellman (HJB) equation solution and derive the approximate optimal control policy. Theoretical analysis proves the closed-loop signals are uniformly ultimately bounded (UUB). Numerical simulations validate the scheme's effectiveness in ensuring optimal control performance while saving resources.