Adaptive And Learning Based Control Of Safety Critical Systems
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Adaptive and Learning-Based Control of Safety-Critical Systems
This book stems from the growing use of learning-based techniques, such as reinforcement learning and adaptive control, in the control of autonomous and safety-critical systems. Safety is critical to many applications, such as autonomous driving, air traffic control, and robotics. As these learning-enabled technologies become more prevalent in the control of autonomous systems, it becomes increasingly important to ensure that such systems are safe. To address these challenges, the authors provide a self-contained treatment of learning-based control techniques with rigorous guarantees of stability and safety. This book contains recent results on provably correct control techniques from specifications that go beyond safety and stability, such as temporal logic formulas. The authors bring together control theory, optimization, machine learning, and formal methods and present worked-out examples and extensive simulation examples to complement the mathematical style of presentation. Prerequisites are minimal, and the underlying ideas are accessible to readers with only a brief background in control-theoretic ideas, such as Lyapunov stability theory.
Formal Methods for Multi-Agent Feedback Control Systems
An introduction to formal methods for feedback control of multi-agent systems with safety and performance guarantees. Multi-agent control systems can accomplish tasks that single-agent systems cannot address, such as aerial surveillance of large areas by a group of drones. In Formal Methods for Multi-Agent Feedback Control Systems, Lars Lindemann and Dimos Dimarogonas provide an accessible introduction to formal methods for feedback control of multi-agent systems. Their book is the first to bridge the gap between formal methods and feedback control for the scalable design of cyber-physical systems. The material covered is intended for scientists, engineers, and students, and no background in formal methods or control theory is required. The authors also highlight future research directions for those working at the intersection of formal methods and control. In control theory, the goal is to design feedback control laws for dynamical systems that achieve control objectives such as stability or forward invariance of sets. Formal methods, on the other hand, provide verification and design techniques for more complex system specifications using temporal logics. However, their high computational cost limits scaling beyond a small number of agents. Besides scalability, another central challenge is to achieve robustness in the system design. Thus, the authors focus on the design of scalable and robust feedback control algorithms for multi-agent control systems under temporal logic specifications.
Advances in Guidance, Navigation and Control
This book features the latest theoretical results and techniques in the field of guidance, navigation, and control (GNC) of vehicles and aircrafts. It covers a wide range of topics, including but not limited to, intelligent computing communication and control; new methods of navigation, estimation, and tracking; control of multiple moving objects; manned and autonomous unmanned systems; guidance, navigation, and control of miniature aircraft; and sensor systems for guidance, navigation and control, etc. Presenting recent advances in the form of illustrations, tables, and text, it also provides detailed information of a number of the studies, to offer readers insights for their own research. In addition, the book addresses fundamental concepts and studies in the development of GNC, making it a valuable resource for both beginners and researchers wanting to further their understanding of guidance, navigation, and control.