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Enhancing Cyber-Physical System Resilience With Safe Data-Driven Control Strategies
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Author(s): Muhammad Usman Tariq (Abu Dhabi University, UAE & University College Cork, Ireland)
Copyright: 2026
Pages: 26
Source title:
Safe Data-Driven Control for Cyber-Physical Systems
Source Author(s)/Editor(s): Adnène Arbi (National Institute of Applied Sciences and Technology, University of Carthage, Tunisia & Laboratory of Mathematical Engineering, Tunisia Polytechnic School, University of Carthage, Tunisia)
DOI: 10.4018/979-8-3373-1832-5.ch004
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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.
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