Secure Change Control for Supply Chain Systems via Dynamic Event Triggered Using Reinforcement Learning under DoS Attacks

Abstract

In this paper, a distributed secure change control scheme for supply chain systems is presented under denial-of-service (DoS) attacks. To eliminate the effect of DoS attacks on supply chain systems, a secure change compensation is designed. A distributed policy iteration method is established to approximate the coupled Hamilton–Jacobi–Isaacs (HJI) equations. Based on the established reinforce–critic–actor (RCA) structure using reinforcement learning (RL), the reinforced signals, performance indicators, and disturbance input are proposed to update the traditional time-triggered mechanism, and the control input is proposed to update the dynamic event-triggered mechanism (DETM). Stability is guaranteed based on the Lyapunov method under secure change control. The simulation results for supply chain systems show the effectiveness of the secure change control scheme and verify the results.