Advanced Nonlinear and Adaptive Control Strategies for CPL-Dominant DC Microgrids: A Comprehensive Review

DC microgrids have emerged as a highly efficient and reliable architecture for integrating renewable energy sources, energy storage systems, and modern electronic loads in applications ranging from data centres and electric vehicles to all-electric ships and more-electric aircraft. A critical challenge in these systems is the presence of Constant Power Loads (CPLs), which arise from tightly regulated power electronic converters. The inherent negative incremental impedance characteristic of CPLs degrades system damping and can lead to voltage instability and collapse. This paper provides a comprehensive review of the control strategies developed to mitigate CPL-induced instability. It systematically classifies and analyzes methods, ranging from conventional passive and linear active damping to advanced nonlinear techniques, including Sliding Mode Control (SMC), Backstepping, Passivity-Based Control (PBC), and Model Predictive Control (MPC). Furthermore, the review examines the latest adaptive and intelligent control strategies, including Reinforcement Learning and Neural Network-based controllers, which provide remarkable adaptability to uncertain and time-varying system conditions. A critical comparative analysis is presented, discussing the trade-offs in robustness, performance, computational cost, and implementation complexity. Finally, key research gaps are identified, and promising future directions—including hybrid control, large-signal stability guarantees, scalable distributed control, and Explainable AI—are discussed. This review serves as a valuable resource for researchers and engineers working towards the design of robust, efficient, and stable next-generation DC power systems.