Decentralized Attack-Resilient CLF-Based Control of Nonlinear DC Microgrids under FDI Attacks

TL;DR

This paper introduces a decentralized control framework for nonlinear DC microgrids that maintains stability and resilience against diverse cyber-physical attacks, including unbounded false-data-injection, using an adaptive Lyapunov-based approach.

Contribution

It develops a novel AR-CLF based Quadratic Program control method that ensures large-signal stability and attack resilience without relying on global information.

Findings

Achieves superior stability under diverse FDI attacks

Handles unbounded control-input perturbations effectively

Demonstrates scalability and resilience through simulations

Abstract

The growing deployment of nonlinear, converter interfaced distributed energy resources (DERs) in DC microgrids demands decentralized controllers that remain stable and resilient under a wide range of cyber-physical attacks and disturbances. Traditional droop or linearized control methods lack resilience and scalability, especially when the system operates in its nonlinear region or faces diverse false-data-injection (FDI) attacks on control inputs. In this work, we develop a Decentralized Attack-Resilient Control Lyapunov Function (AR-CLF) based Quadratic Program (QP) control framework for nonlinear DC microgrids that ensures large-signal stability in a fully decentralized manner. Built upon the port-Hamiltonian representation, the proposed controller dynamically compensates diverse attacks including exponentially unbounded control-input perturbations beyond the bounded-attack regime commonly assumed in existing methods, through an adaptive resilience term, without requiring global information. Simulations validate that the AR-CLF based QP controller achieves superior stability and resilience against unbounded attacks, paving the way for scalable, attack-resilient, and physically consistent control of next-generation DC microgrids.