Automation-Driven Robust Neural Predictive Control for DC Shipboard Microgrids

The reliable and stable operation of modern shipboard power systems (SPSs) is crucial for mission success, particularly when faced with high-power pulsed loads and complex system interactions. While nonlinear model predictive control (NMPC) offers effective stabilization and robustness to bounded disturbances via its variant quasi-infinite-horizon NMPC (QIH-NMPC), it imposes a computational burden that limits real-time applicability for mission-critical SPSs at the microseconds level. This paper proposes a robust data-driven predictive control (DDPC) approach that leverages deep neural networks to approximate the QIH-NMPC control law, enabling fast-response and automated power management for medium-voltage direct current (MVDC) shipboard microgrids (MGs) that integrate synchronous generators, batteries, and supercapacitors. The proposed method includes a rigorous control design with theoretical guarantees and is validated under out-of-distribution (OOD) scenarios, aggressive load fluctuations with stochastic noise, stealthy false data injection attacks via numerical simulations, and fault conditions in a real-time simulator. Across diverse unseen disturbances, DDPC yields an average inference time of $6.91~\mu s$ , preserves control robustness by stabilizing the system within 0.05 seconds. It achieves a 97.8% reduction in computational load and improves control smoothness by 31% compared with NMPC, while also converging 50% faster than proportional-integral (PI) control. These findings highlight DDPC’s potential for automated, resilient, and real-time SPS control. Note to Practitioners—While NMPC offers strong performance, its computational demands hinder real-time feasibility, especially in nonlinear, fast-changing systems. This work introduces a practical, real-time control solution by learning robust NMPC policies offline using a data-driven (neural-based) predictive control framework. The trained model was tested beyond its training data to handle load surges, failures, and cyberattacks. The proposed method eliminates online optimization, offering fast control actions suitable for embedded systems. Practitioners can deploy the trained controller on real-time platforms (e.g., OPAL-RT and FPGAs) to manage voltage restoration and stability in shipboard microgrids. This approach is extendable to Uncrewed Aerial Vehicles (UAVs), electric vehicles, or smart grids, wherever fast, stable control with limited onboard computation is needed.