Survey on data-driven control and its application in cyber-physical energy systems

作者：Wenjie Liu, Mengfan Zhang, Qianwen Xu, Lihua Xie · 发表于：Cyber-Physical Energy Systems · 年份：2025 · DOI：10.1016/j.cpes.2025.08.004 · 被引用次数：6 · 研究领域：Smart Grid Security and Resilience、Fault Detection and Control Systems、Advanced Data Processing Techniques

Data-driven control has rapidly matured into a powerful complement to classical model-based paradigms. Among the most successful approaches are nonparametric, trajectory-centric methods built on Willems et al.’s fundamental lemma and model-free reinforcement learning (RL) schemes. In this survey, we reverse the usual presentation order to reflect their differing assumptions and scopes. First, this paper provides a unified treatment of fundamental lemma-based methods, which require knowledge of the system structure (LTI or suitably lifted nonlinear) but offer rigorous guarantees by replacing explicit model identification with input-output trajectory representations. This paper covers state-feedback synthesis for both linear and nonlinear systems, extensions to model predictive control that directly leverage data, and recent advances in data-driven state estimation under noise-free and noisy measurements. This paper then turns to RL-based control, which lifts the structural constraints entirely — trading formal stability proofs for broad applicability to complex and uncertain cyber-physical energy systems (CPESs) dynamics. This paper reviews key RL architectures, policy-and value-based algorithms, and their practical challenges in energy applications. Recognizing the network-induced vulnerabilities of modern grids, this paper also surveys data-driven defense strategies against Denial-of-Service and false-data-injection attacks, from anomaly detection to resilient control. To il...