Robust Adaptive Dynamic Positioning: An Asynchronous Actor and Critic Approach with Meta-Driven Radial Function Network

Dynamic Positioning systems are crucial for modern marine vessels to maintain positions or track trajectories under environmental disturbances. Traditional model-based and neural network control schemes often suffer from heavy computational burdens, low-velocity nonlinearities, and chattering near decision boundaries during waypoint transitions, which can trigger actuator saturation. To address these challenges, this paper proposes an enhancing robust adaptive control algorithm. Specifically, a model-free control framework is developed by employing an asynchronous deep Actor–Critic neural network with multi-layer perceptron for high-precision policy approximation in continuous spaces. To accelerate convergence, an online meta-driven radial basis function network is proposed for adaptive reward shaping, optimized by the Adam scheme. Furthermore, at the guidance level, a hysteresis state machine and an adaptive damping reference model are designed to decouple wave-induced high-frequency chattering and eliminate thrust saturation. By applying dynamic surface control, the proposed scheme avoids complex thrust allocation calculations. The proposed method enhances system autonomy and ensures smooth transient behavior while maintaining compatibility with standard marine hardware.