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 syste