menu
{ "item_title" : "Learning-Enabled Autonomous Systems", "item_author" : [" Jianglin Lan "], "item_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.", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/1/04/130/277/1041302770_b.jpg", "price_data" : { "retail_price" : "115.99", "online_price" : "115.99", "our_price" : "115.99", "club_price" : "115.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Learning-Enabled Autonomous Systems|Jianglin Lan

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

PRE-ORDER NOW:
local_shippingShip to Me
Preorder. This item will be available on September 29, 2026 .
FREE Shipping for Club Members help

Overview

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.

This item is Non-Returnable

Details

  • ISBN-13: 9781041302773
  • ISBN-10: 1041302770
  • Publisher: CRC Press
  • Publish Date: September 2026
  • Page Count: 208

Related Categories

You May Also Like...

    1

BAM Customer Reviews