Optimal Iterative Learning Control: A Practitioner's Guide
暫譯: 最佳迭代學習控制：實務指南
Chu, Bing, Owens, David H.
- 出版商: Springer
- 出版日期: 2025-06-13
- 售價: $5,840
- 貴賓價: 9.5 折 $5,548
- 語言: 英文
- 頁數: 356
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 3031802357
- ISBN-13: 9783031802355
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商品描述
This book introduces an optimal iterative learning control (ILC) design framework from the end user's point of view. Its central theme is the understanding of model dynamics, the construction of a procedure for systematic input updating and their contribution to successful algorithm design. The authors discuss the many applications of ILC in industrial systems, applications such as robotics and mechanical testing.
The text covers a number of optimal ILC design methods, including gradient-based and norm-optimal ILC. Their convergence properties are described and detailed design guidelines, including performance-improvement mechanisms, are presented. Readers are given a clear picture of the nature of ILC and the benefits of the optimization-based approach from the conceptual and mathematical foundations of the problem of algorithm construction to the impact of available parameters in making acceleration of algorithmic convergence possible. Three case studies on robotic platforms, an electro-mechanical machine, and robot-assisted stroke rehabilitation are included to demonstrate the application of these methods in the real-world.
With its emphasis on basic concepts, detailed design guidelines and examples of benefits, Optimal Iterative Learning Control will be of value to practising engineers and academic researchers alike.
商品描述(中文翻譯)
本書從最終使用者的角度介紹了一個最佳迭代學習控制（ILC）設計框架。其核心主題是理解模型動態、構建系統性輸入更新程序及其對成功算法設計的貢獻。作者討論了ILC在工業系統中的多種應用，包括機器人技術和機械測試等應用。
本書涵蓋了多種最佳ILC設計方法，包括基於梯度的ILC和範數最優ILC。描述了它們的收斂性質，並提供了詳細的設計指導方針，包括性能改進機制。讀者將清楚了解ILC的本質以及基於優化的方法的好處，從算法構建問題的概念和數學基礎到可用參數對加速算法收斂的影響。本書還包括三個案例研究，涉及機器人平台、一台電機械機器和機器人輔助中風康復，以展示這些方法在現實世界中的應用。
《最佳迭代學習控制》強調基本概念、詳細的設計指導和效益示例，對於實務工程師和學術研究者都將具有價值。
作者簡介
Dr Bing Chu is an associate professor in Electronics and Computer Science at University of Southampton. Before joining University of Southampton in 2012, he was a postdoctoral researcher at University of Oxford (2010-2012). He teaches modules in the general physics, signals, systems and control area at undergraduate/postgraduate level. He has authored or co-authored 70 peer-reviewed scientific publications. He has been the recipient of many awards including the prestigious UKACC best paper prize and Certificate of Merit for IET Control and Automation Doctoral Dissertation Prize. He is a regular referee for a number of international journals and conferences. His current research interests include analysis and control of large scale networked systems, iterative and repetitive control, learning control, applied optimisation theory and their applications.
David H. Owens is a professor at University of Sheffield, UK and Zhengzhou University, China. He has 50 years of experience of Control Engineering theory and applications in areas including nuclear power, robotics and mechanical test. His research has included multivariable frequency domain theory and design, the theory of multivariable root loci, contributions to robust control theory, theoretical methods for controller design based on plant step data and involvement in aspects of adaptive control, model reduction and optimization-based design. His early experience of modelling and analysis of systems with repetitive dynamics originally arising in control of underground coal cutters led to substantial contributions (with collaborator E. Rogers and others) in the area of repetitive control systems (as part of 2D systems theory) but more specifically, since 1996, in the area of iterative learning control when he introduced the use of optimization to the ILC community in the form of "norm optimal iterative learning control". Since that time he has added considerable detail and depth to the approach and introducing the ideas of parameter optimal iterative learning to simplify the implementations. Applications have included industrial projects in automotive/mechanical tests and the development of data analysis tools for control of gantry robots and stroke rehabilitation equipment with collaborators at Southampton University. Professor Owens was elected a Fellow of the UK Royal Academy of Engineering for his contributions to knowledge in these and other areas.
作者簡介(中文翻譯)
Dr Bing Chu 是南安普敦大學電子與計算機科學的副教授。在2012年加入南安普敦大學之前，他曾於牛津大學擔任博士後研究員（2010-2012）。他教授本科及研究生層級的一般物理、信號、系統與控制相關的課程。他已發表或共同發表70篇經過同行評審的科學出版物。他獲得了多項獎項，包括享有盛譽的英國自動控制協會最佳論文獎和IET控制與自動化博士論文獎的優異證書。他是多個國際期刊和會議的常任審稿人。他目前的研究興趣包括大規模網絡系統的分析與控制、迭代與重複控制、學習控制、應用優化理論及其應用。
David H. Owens 是英國謝菲爾德大學和中國鄭州大學的教授。他在控制工程理論及其應用方面擁有50年的經驗，涵蓋核能、機器人和機械測試等領域。他的研究包括多變量頻域理論與設計、多變量根軌跡理論、對穩健控制理論的貢獻、基於植物步進數據的控制器設計理論方法，以及自適應控制、模型簡化和基於優化的設計等方面的參與。他早期對具有重複動態的系統建模與分析的經驗，最初源於地下煤切割機的控制，對重複控制系統（作為2D系統理論的一部分）做出了重要貢獻（與合作者E. Rogers等人合作），但更具體地，自1996年以來，他在迭代學習控制領域的貢獻，當時他將優化的使用引入到ILC社群，形成了“範數最優迭代學習控制”。自那時以來，他為該方法增添了相當多的細節和深度，並引入了參數最優迭代學習的概念，以簡化實施。應用案例包括汽車/機械測試的工業項目，以及與南安普敦大學的合作者共同開發的用於控制龍門機器人和中風康復設備的數據分析工具。Owens教授因其在這些及其他領域的知識貢獻而被選為英國皇家工程院院士。
