Transition‐Aware Q‐Learning for Robust Tracking in Networked Control Systems with Fading Channels: Application to Leader−Follower Vehicle Control

In various industrial domains, such as intelligent transportation systems, maintaining accurate tracking performance in networked control systems (NCS) operating over fading wireless channels remains a critical challenge. The fundamental difficulty arises from the stochastic and temporally correlated nature of wireless links, which limits the effectiveness of existing control strategies. To address this problem, we introduce a transition‐aware Q‐learning (TA‐QL) framework that enables model‐free robust tracking control of NCS subject to fading‐induced uncertainties. The proposed approach learns optimal policies directly from networked data while preserving the Markovian dependencies among network states, without requiring explicit models or solving coupled algebraic Riccati equations. We rigorously prove that the learned policies ensure mean‐square stability and satisfy the disturbance attenuation criterion. Extensive simulations on a leader–follower vehicle spacing control scenario over realistic 5G fading channels validate the effectiveness of TA‐QL. Compared with baseline schemes, including subsystem transformation‐based approach, TA‐QL improves convergence rate and data efficiency by approximately 54%. Overall, the proposed method bridges robust control and data‐driven learning, offering a practical solution for industrial applications of NCS.