Topics include set-based state estimation in varied dynamical system settings, sliding-mode predictive and state-feedback control, innovative optimization algorithms, zonotopic fault detection and identification, as well as distributed moving horizon estimation. The proposed methods are illustrated through practical simulation studies in robotics, autonomous vehicles, fuel cell systems, and sensor networks.

Intended for researchers, engineers, and graduate students in control systems, applied mathematics, and various engineering disciplines, this book offers both a rigorous foundation and cutting-edge approaches for addressing uncertainty in complex dynamical systems.