Enhancing Cyber-Physical System Resilience With Safe Data-Driven Control Strategies

Abstract

This chapter explores strategies for enhancing the resilience of Cyber-Physical Systems (CPS) through safe and data-driven control mechanisms. As CPS become increasingly reliant on artificial intelligence (AI) and machine learning (ML) for real-time decision-making, ensuring system safety, security, and reliability becomes a critical challenge. The chapter examines various AI-powered control techniques, including Model Predictive Control (MPC), reinforcement learning, and anomaly detection, to optimize system performance while mitigating risks such as adversarial attacks, biases, and unpredictability. Additionally, it highlights the role of edge computing and distributed control architectures in reducing latency and improving fault tolerance. The chapter also addresses cybersecurity threats that impact CPS, including data breaches, cyberattacks, and network vulnerabilities

Keywords Adversarial machine learning, Anomaly detection, Computer architecture, Cyber Physical System, Cybersecurity