Sampling-Based Planning and Predictive Control for Energy Management of a Shipboard Integrated Power System With High Ramp Rate Load

Abstract Integrated power systems (IPS) aboard electrified ships require energy management strategies that ensure safe, autonomous operation. Next-generation platforms are expected to make such decisions with minimal human oversight. However, the complex, multidomain, multitimescale dynamics of IPS—combined with high ramp rate loads like electronic warfare systems—pose significant challenges. Additionally, these systems often face uncertain, time-varying, mission-specific constraints that create nonconvex feasible regions, limiting the effectiveness of conventional energy management approaches. This work presents a hierarchical, two-stage framework for safe and adaptive energy management in shipboard IPS. At the upper level, a sampling-based rapidly exploring random tree (RRT) algorithm identifies feasible long-term power and energy trajectories within nonconvex constraint spaces. At the lower level, a robust model predictive control (MPC) scheme ensures accurate trajectory tracking with bounded error, accommodating the dynamics of major components while maintaining constraint satisfaction. The framework is demonstrated on a two-zone IPS model with a high ramp rate load. Simulation results show the proposed planner efficiently generates feasible mission plans that adapt to evolving constraints, while the MPC controller ensures reliable tracking and constraint adherence. By bridging long-term planning with short-term control, this architecture enables safe, flexible, and autonomous operation of complex shipboard power systems. It addresses key limitations of existing strategies in managing nonconvex constraints and dynamic mission contexts, making it well-suited for resilient autonomy in future maritime platforms.