Si Wu – författare

Practical tools and techniques to achieve objectives in safety-critical control This book offers a systematic framework for the safety-critical control of nonlinear uncertain systems, with key contributions such as the development of novel small-gain synthesis and feasible-set reshaping techniques to address interactions between the nominal controlled system and dynamic uncertainties. Incorporating recent advancements in the field, this book showcases the strengths of the proposed framework by tackling key theoretical challenges in safety-critical control across multiple benchmark systems. It further highlights the real-world impact of the developed methods and algorithms through practical applications involving vehicles, quadrotors, and robotic manipulators. All results are supported by laboratory experiments with clear explanations. Written by a team of highly qualified authors, Robust Safety-Critical Control includes: Insights into the challenges of designing controllers that maintain safety while achieving desired objectives, in the presence of uncertainties, nonlinear dynamics, and multiple constraintsA mathematical foundation for robust safety-critical control, presented in the appendices covering quadratic optimization, Lyapunov stability theory, input-to-state stability, and the nonlinear small-gain theoremChapter-by-chapter problem formulations and detailed, rigorous developments of the theory and methods, guiding the reader through the process of addressing the fundamental challengesComprehensive system setups for safety-critical control simulations and experimentsReady-to-use code implementations for key algorithms, including the feasible-reshaping technique and small-gain control methodsRobust Safety-Critical Control is an excellent reference for researchers and graduate students in systems and control, robotics, transportation and AI seeking to expand their knowledge bases. The text is also highly valuable for engineers and practitioners in control engineering, civil and urban engineering, robotics, and manufacturing.

This book provides an overview of neural information processing research, which is one of the most important branches of neuroscience today. Neural information processing is an interdisciplinary subject, and the merging interaction between neuroscience and mathematics, physics, as well as information science plays a key role in the development of this field. This book begins with the anatomy of the central nervous system, followed by an introduction to various information processing models at different levels. The authors all have extensive experience in mathematics, physics and biomedical engineering, and have worked in this multidisciplinary area for a number of years. They present classical examples of how the pioneers in this field used theoretical analysis, mathematical modeling and computer simulation to solve neurobiological problems, and share their experiences and lessons learned. The book is intended for researchers and students with a mathematics, physics or informatics background who are interested in brain research and keen to understand the necessary neurobiology and how they can use their specialties to address neurobiological problems. It is also provides inspiration for neuroscience students who are interested in learning how to use mathematics, physics or informatics approaches to solve problems in their field.