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Provides a comprehensive overview of the most common approaches used to design distributed optimization algorithms, together with the theoretical analysis of the main schemes in their basic version. The book identifies and formalizes classes of problem set-ups that arise in motivating application scenarios.

Provides a comprehensive survey of intelligent tools for analysis and design that take fundamental steps towards achieving resilient operation in Cyber-Physical Systems. The authors investigate the challenges of achieving reliable control and estimation over networks, particularly in the face of uncertainty and resource constraints.

Formalizes the ecologist's regime shift concept that allows the identification of two distinct regime shift mechanisms, shock-induced and bifurcation-induced regime shift, before defining ecological resilience as a regime transition system.

Provides the reader with an accessible primer on a new direction in control theory still in its infancy, namely Learning-Based Control Theory, that is closely tied to the literature of safe Reinforcement Learning and Adaptive Dynamic Programming.

Describes the use of principles of reinforcement learning (RL) to design feedback policies for continuous-time dynamical systems that combine features of adaptive control and optimal control. The authors give an insightful introduction to reinforcement learning techniques that can address various control problems.

Describes in detail the research on fault diagnosis, opacity analysis and enhancement, and cyber security analysis and enforcement, within suitable discrete event system modelling frameworks. In each case, the authors describe basic problem statements and key concepts, and then point out the key challenges in each research area.