Learning-Enabled Autonomous Systems : Control, Verification, and Monitoring

個数：

予約

Learning-Enabled Autonomous Systems : Control, Verification, and Monitoring

Lan, Jianglin

ウェブストア価格 ¥25,966（本体¥23,606）
CRC Press（2026/09発売）
外貨定価 US$ 115.99
ポイント 236pt

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製本 Hardcover:ハードカバー版／ページ数 208 p.
言語 ENG
商品コード 9781041302773

Full Description

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.

1 Introduction 2 Data-Driven Reachability-Based Model Predictive Control 3 Data-Driven Dual-Loop Model Predictive Control 4 Data-Driven Constrained Control Policy Learning 5 Data-Driven Nonlinear Control Using Nonlinearity Cancellation 6 Data-Driven Sliding Mode Control 7 Learning Observer-Based Control 8 Verification of NN-Controlled Linear Systems 9 Learning Lyapunov Barrier Certificate for Nonlinear Systems 10 Runtime Monitoring for NN-Controlled Linear Systems 11 Runtime Monitoring for NN-Controlled Nonlinear Systems References