Assuring Safe Operation of Robotic Systems Under Uncertainty: Control and Learning Methods
暫譯: 在不確定性下確保機器人系統的安全運行：控制與學習方法

Li, Cong, Wang, Yongchao, Liu, Fangzhou

Assuring Safe Operation of Robotic Systems Under Uncertainty: Control and Learning Methods
  • 出版商: CRC
  • 出版日期: 2025-11-28
  • 售價: $4,470
  • 貴賓價: 9.5 折 $4,247
  • 語言: 英文
  • 頁數: 114
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 1041141203
  • ISBN-13: 9781041141204
  • 相關分類: Reinforcement

    • 海外代購書籍(需單獨結帳)

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商品描述

Assuring Safe Operation of Robotic Systems under Uncertainty: Control and Learning Methods applies set-theoretic and reinforcement learning approaches to formulate, analyze, and solve the challenge of ensuring safe operation of robotic systems in an uncertain environment.

The authors adopt learning-supported, set-theoretic methods--specifically, the barrier Lyapunov function and the control barrier function--to achieve desirable robust safety with guaranteed performance in continuous-time nonlinear control applications. They also combine reinforcement learning with control theory to ensure safe learning and optimization. The reinforcement learning-based optimization framework incorporates safety and robustness guarantees by applying theoretical analysis tools from the field of control.

This book will be of interest to researchers, engineers, and students specializing in robot planning and control.

商品描述(中文翻譯)

《在不確定性下確保機器人系統安全運行：控制與學習方法》應用集合論和強化學習方法來制定、分析和解決在不確定環境中確保機器人系統安全運行的挑戰。

作者採用學習支持的集合論方法——具體而言，障礙李雅普諾夫函數和控制障礙函數——以實現期望的穩健安全，並在連續時間非線性控制應用中保證性能。他們還將強化學習與控制理論相結合，以確保安全的學習和優化。基於強化學習的優化框架通過應用控制領域的理論分析工具，納入安全性和穩健性保證。

本書將吸引專注於機器人規劃和控制的研究人員、工程師和學生。

作者簡介

Cong Li earned a PhD from the Chair of Automatic Control Engineering, Technical University of Munich, Germany in 2022. He was also a research associate at the Chair of Automatic Control Engineering, Technical University of Munich.

Yongchao Wang is at the Xi'an Research Institution of Hi-Technology and a professor at the School of Aerospace Science and Technology, Xidian University, Xi'an, China. He was at the Chair of Automatic Control Engineering, Technical University of Munich, Germany.

Fangzhou Liu received the Doktor-Ingenieur degree in electrical engineering from the Technical University of Munich, Germany in 2019. He was a lecturer and a research fellow at the Chair of Automatic Control Engineering, Technical University of Munich, Germany. He is now a full professor at the School of Astronautics, Harbin Institute of Technology, Harbin, China.

Xinglong Zhang earned a BE in mechanical enineering from Zhejiang University, Hangzhou, China in 2011 and a PhD in system and control from the Politecnico di Milano, Italy, 2018. He is presently an associate professor at the College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China. His research interests include Koopman operators, learning-based model predictive control, reinforcement learning, and approximate dynamic programming, and their applications in automotive systems.

作者簡介(中文翻譯)

李聰於2022年在德國慕尼黑工業大學自動控制工程系獲得博士學位。他曾擔任德國慕尼黑工業大學自動控制工程系的研究助理。

王永超在西安高科技研究院任職，並且是中國西安西電大學航空科學與技術學院的教授。他曾在德國慕尼黑工業大學自動控制工程系工作。

劉方舟於2019年在德國慕尼黑工業大學獲得電氣工程的Doktor-Ingenieur學位。他曾擔任德國慕尼黑工業大學自動控制工程系的講師和研究員。現在他是中國哈爾濱工業大學航天學院的全職教授。

張興龍於2011年在中國杭州浙江大學獲得機械工程學士學位，並於2018年在意大利米蘭理工大學獲得系統與控制的博士學位。他目前是中國長沙國防科技大學智能科學與技術學院的副教授。他的研究興趣包括Koopman算子、基於學習的模型預測控制、強化學習和近似動態規劃，以及它們在汽車系統中的應用。

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