Introduction -- Optimal DoS Attack Scheduling for CPSs -- Active Defense Control of CPSs via Sliding Mode -- Learning Tracking Control for CPSs -- Intelligent Control for Nonlinear Networked Control Systems -- Reliable Filtering of Sensor Networks -- Secure Estimation for CPSs via Sliding Mode -- Zero-Sum Game Based Optimal Secure Control -- Proactive Secure Control for CPSs. Fault-Tolerant Tracking Control for Nonstrict-Feedback Systems -- Deep Reinforcement Learning Control Approach to Mitigating Attacks -- Conclusion and Further Work.

This book analyzes the secure problems of cyber-physical systems from both the adversary and defender sides. Targeting the challenging security problems of cyber-physical systems under malicious attacks, this book presents some recent novel secure state estimation and control algorithms, in which moving target defense scheme, zero-sum game-theoretical approach, reinforcement learning, neural networks, and intelligent control are adopted. Readers will find not only the valuable secure state estimation and control schemes combined with the approaches aforementioned, but also some vital conclusions for securing cyber-physical systems, for example, the critical value of allowed attack probability, the maximum number of sensors to be attacked, etc. The book also provides practical applications, example of which are unmanned aerial vehicles, interruptible power system, and robot arm to validate the proposed secure algorithms. Given its scope, it offers a valuable resource for undergraduate and graduate students, academics, scientists, and engineers who are working in this field.