DC Shipboard Microgrid Control Using Online Multilayer Neural Network Lifelong Learning

Abstract

This work proposes an adaptive optimal control scheme for DC shipboard power systems (SPS) with multiple distributed generators (DGs), leveraging a multilayer neural network (MNN) to enhance the performance of a supplementary energy storage system (ESS) under pulsed power load (PPL) conditions. PPLs impose large, short-duration power demands that challenge system stability due to nonlinear dynamics and operational constraints. The proposed reinforcement learning (RL) framework with lifelong hybrid learning (LHL) addresses these challenges by pursuing three objectives: rapid ESS charging, DC bus voltage regulation, and proportional load current sharing among DG converters. An actor-critic MNN approximates the value function and control policy, where the critic network is updated using a hybrid scheme that refines weights both at and within sampling instants for faster convergence. A weight consolidation mechanism further enables lifelong learning, mitigating catastrophic forgetting and reducing overall cost, while the actor adapts using control input errors. Experimental validation on the OPAL-RT OP4512 hardware-in-the-loop (HIL) platform demonstrates that the proposed controller effectively achieves the control objectives under dynamic load conditions.

Department(s)

Electrical and Computer Engineering

Second Department

Computer Science

Comments

Office of Naval Research, Grant N00014-24-1-2338

Keywords and Phrases

DC shipboard power system; HIL; neural networks; pulsed power load (PPL); reinforcement learning

International Standard Book Number (ISBN)

979-8-3315-9381-0; 979-8-3315-9382-7

International Standard Serial Number (ISSN)

0743-1619

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2026 Institute of Electrical and Electronics Engineers, All rights reserved.

Publication Date

01 Jan 2026

Link to Full Text

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