A Physics-Informed Non-Markovian Deep Learning Model for Robust Ship Motion Prediction Under Non-Ideal Observations

High-fidelity ship dynamics models are essential for the reliable operation of maritime autonomous systems. However, existing Markov-based maneuvering models and purely data-driven predictors struggle to capture hydrodynamic memory and degrade under non-ideal sensing. To address these challenges, this paper proposes a novel approach for robust ship motion prediction, the Non-Markovian Memory-Augmented Environment-Perceived and Physics-Informed Network (NMA-EPIN). This method explicitly models long-term hydrodynamic dependencies through a memory-augmented architecture. Within NMA-EPIN, a Control-Physics-Informed Neural Network (CPINN) paradigm enforces velocity–position kinematic consistency and control-logic alignment as soft constraints, suppressing cumulative drift under degraded observations. Experiments on a high-fidelity simulated dataset show that NMA-EPIN attains an average coefficient of determination R2=0.977 under nominal conditions, effectively eliminating the position drift observed in baselines. Under extreme compound perturbations (50% sensor noise, packet loss, and delays), NMA-EPIN retains R2≈0.91, which significantly outperforms the baselines.