Learning-Enabled Autonomous Systems Control Verification And Monitoring

This book examines how to design intelligent systems that are not only adaptive but also safe and reliable. This book bridges the gap between traditional control theory and modern data-driven learning presenting a unified framework for creating autonomous systems capable of robust decision-making in uncertain and dynamic environments. Learning-Enabled Autonomous Systems: Control Verification and Monitoring stands out as a unique resource for designing trustworthy autonomous systems. It introduces data-driven control methods that allow systems to learn from real-world data enabling adaptability and intelligence without sacrificing mathematical rigor. The book explores advanced control strategies for nonlinear systems ensuring computational practicality for real-world applications. It also provides innovative techniques for verifying neural network controllers and safeguarding system performance through runtime monitoring frameworks. By uniting control theory machine learning and systems verification this book offers a holistic approach to creating systems that are not only intelligent but also resilient transparent and dependable. It includes case studies algorithmic insights and design guidelines that connect theoretical principles to hands-on engineering practice. This book is tailored for graduate students researchers and practitioners in control systems robotics artificial intelligence and systems engineering. It is ideal for those seeking to deepen their understanding of learning-enabled control systems whether for academic study or real-world application. |Learning-Enabled Autonomous Systems Control Verification and Monitoring