Estimation and Control of Markovian Jump Systems: Applications in Industrial Engineering provides a comprehensive exploration of Markovian Jump Systems (MJSs), a unique class of hybrid systems that model dynamic processes exposed to unpredictable variations, such as random failures, environmental disturbances, and subsystem changes. With a focus on systems with deficient transition descriptions, the book bridges the gap between theoretical insights and practical applications, making it an essential tool for tackling complex challenges in industrial and systems engineering. Packed with innovative methodologies, the book offers readers a deep dive into fixed-order H-infinity filtering, delay-dependent control strategies, and multidimensional filter design for MJSs. It introduces robust approaches to handle uncertain and partially unknown transition rates and probabilities, leveraging advanced techniques such as convex optimization, Lyapunov-Krasovskii functionals, and linearization procedures. Divided into three sections, filtering for continuous- and discrete-time systems, delay-dependent control strategies, and state estimation frameworks for two-dimensional systems, the book is enriched with illustrative examples, detailed analyses, and synthesis techniques, providing practical solutions for real-world engineering challenges. Tailored for practitioners, researchers, and graduate students in industrial engineering, systems engineering, operations research, and related fields such as electrical, mechanical, aerospace, and computer engineering, the content is particularly valuable for those with a solid background in mathematics, matrix theory, probability, optimization techniques, and control system theory. Whether focused on theoretical exploration or practical applications for MJSs, it offers the tools and insights necessary to deepen expertise in this dynamic and evolving domain.
