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 constraints
• A 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 theorem
• Chapter-by-chapter problem formulations and detailed, rigorous developments of the theory and methods, guiding the reader through the process of addressing the fundamental challenges
• Comprehensive system setups for safety-critical control simulations and experiments
• Ready-to-use code implementations for key algorithms, including the feasible-reshaping technique and small-gain control methods

Robust 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.

Tengfei Liu is a Professor at Northeastern University, China. He has been deeply engaged in developing and applying nonlinear control methods to engineering systems.

Si Wu is a Postdoctoral Researcher at the State Key Laboratory of Synthetical Automation for Industrial Processes, Northeastern University, China, and a key contributor to the feasible-set reshaping technique for safety-critical control.

Zhi Liu is a PhD student at Northeastern University, China, focusing on developing safety-critical control methods for mechanical systems that fully leverage their inherent energy properties.

Zhong-Ping Jiang is an Institute Professor at New York University, USA. He has made seminal contributions to stability theory, nonlinear control, robust adaptive dynamic programming, learning-based control, and their applications to information, mechanical, transportation and biological systems.