A Dual‐Strategy Framework for Ship Course‐Keeping: Multi‐Constraint All Stability Region Synthesis With ASR‐Constrained Q‐Learning Control

ABSTRACT Navigating marine vessels along precise trajectories remains a formidable control challenge due to high‐order inertia, nonlinear actuator constraints, and stochastic environmental disturbances. This work presents a robust dual‐strategy framework for ship course‐keeping by integrating classical frequency‐domain design with reinforcement learning. The proposed approach consists of two methods: (i) a multi‐constraint all‐stability region (ASR)‐based controller design and (ii) an ASR‐guided Q‐learning control architecture. By using the stability boundary locus approach, the ASR is constructed in the ‐plane. In addition, control‐effort constraints are directly incorporated into the ASR framework to ensure the rudder angle remains within permissible operating limits. Controller gains are selected from the feasible intersection region formed by the imposed design constraints. These analytically derived gains are then used to initialize the Q‐learning agent, enabling the learning process to begin from a stable and constraint‐satisfying operating condition. The proposed hybrid framework utilizes a reward‐based adaptive mechanism to enhance transient and steady‐state tracking while maintaining smooth and bounded control action. Stability, robustness, and rudder saturation constraints are embedded within the controller design space, ensuring that the learning process remains confined to feasible and analytically stable solutions. As a result, the proposed approach improves tracking performance without imposing excessive control effort or actuator burden. Comparative analysis with recently reported control strategies demonstrates that the proposed approach offers substantial improvements in closed‐loop performance, as evidenced by the results presented. For normal sea states, ASR and Q‐ASR controllers achieve reductions in settling time by , , and , compared to nonlinear decoration and switching feedback methods, respectively. The proposed controller efficacy was verified on a 189‐meter Yupeng vessel via real‐time controller hardware‐in‐the‐loop (C‐HIL) experiments using the C2000 Delfino F28379D platform. The proposed framework derives optimal control policies in simulation that can be efficiently implemented on standard embedded platforms. Resulting controllers offer a practical solution for autonomous ship autopilots while improving fuel economy and reducing actuator wear during maritime operations.