Nonlinear Control of Uncertain Systems: Conventional and Learning-Based Alternatives with Python
暫譯: 不確定系統的非線性控制：傳統與基於學習的替代方案（使用Python）

Alamir, Mazen

Nonlinear Control of Uncertain Systems: Conventional and Learning-Based Alternatives with Python
  • 出版商: Springer
  • 出版日期: 2026-05-25
  • 售價: $8,710
  • 貴賓價: 9.8 折 $8,535
  • 語言: 英文
  • 頁數: 623
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 3031932862
  • ISBN-13: 9783031932861
  • 相關分類: Python

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相關主題

商品描述

This book provides scalable, effective and real-world-compatible methods and algorithms for the control and extended estimation of uncertain nonlinear systems against a backdrop of often-unconventional problems. The author provides advice on choosing which solution is most relevant to the desired control objectives, the nature of present uncertainties and the impact on closed-loop performance.

The book introduces its key paradigms step by step and then presents the family of candidate solutions in detail along with associated python scripts. It helps the reader develop a critical and comparative point of view and thus to distinguish the best choice of solutions, some of which prove to be conventional and others to employ advanced learning-based methods. This book shows how each category applies to specific groups of problems, but the choice is made based on pragmatic assessments of efficiency and efficacy rather than on dogmatic adherence to the benefits of one or the other.

All of the concepts and solutions described in the text are illustrated using significantly challenging problems, wherever possible with real-world relevance. Solutions are implemented using Python scripts, freely downloadable from the author's GitHub account. Practical features such as messages, cautions, summaries and important comments are clearly presented to aid reading, retention and recall.

Nonlinear Control of Uncertain Systems appeals to both academics and professional practitioners studying and developing nonlinear industrial control systems; its critical comparative appraisal and detailed range of solutions help readers to navigate a complex taxonomy of systems and to find the right solution--learning-based or conventional--for the problems before them.

商品描述(中文翻譯)

本書提供可擴展、有效且與現實世界相容的方法和演算法，用於控制和擴展不確定非線性系統的估計，背景是經常不尋常的問題。作者提供了選擇最相關解決方案的建議，這些解決方案與所需的控制目標、當前不確定性的性質以及對閉環性能的影響有關。

本書逐步介紹其關鍵範式，然後詳細呈現候選解決方案的家族及相關的 Python 腳本。它幫助讀者發展批判性和比較的觀點，從而區分最佳的解決方案選擇，其中一些被證明是傳統的，而其他則採用先進的基於學習的方法。本書展示了每個類別如何應用於特定問題組，但選擇是基於對效率和效能的務實評估，而不是對某一種或另一種好處的教條式堅持。

文本中描述的所有概念和解決方案都使用顯著具有挑戰性的問題進行說明，並在可能的情況下與現實世界相關。解決方案使用 Python 腳本實現，這些腳本可以從作者的 GitHub 帳戶免費下載。實用的特徵，如消息、警告、摘要和重要評論，清晰地呈現，以幫助閱讀、記憶和回憶。

《不確定系統的非線性控制》吸引了學術界和專業實踐者，這些人研究和開發非線性工業控制系統；其批判性比較評估和詳細的解決方案範圍幫助讀者導航複雜的系統分類法，並找到適合他們面臨問題的正確解決方案——無論是基於學習的還是傳統的。

作者簡介

Mazen Alamir is Research Director at CNRS, France. He graduated in mechanics (Grenoble, 1990) and avionics (Toulouse 1992). He received his Ph.D. in nonlinear model predictive control in 1995 from Grenoble Institute of Technology. He served as Head of the nonlinear systems and complexity research group at the Control System Department, University of Grenoble-Alpes. His main research topics are nonlinear model predictive control, nonlinear moving-horizon estimators and blind anomaly detection in engineering equipments. He was Member of the IFAC technical committee on nonlinear systems as well as the IEEE Conference Editorial Board and served as Associate Editor of the IEEE Transactions on Automatic Control (2008-2021). He organized the first IFAC workshop on NMPC for Fast Systems, Grenoble 2006. He is Co-Founder and Scientific Advisor of the startup Amiral-Technologies, specialized in AI algorithms for industrial predictive maintenance and diagnosis, and Winner of the 2017 GE-digital industrial challenge.

作者簡介(中文翻譯)

Mazen Alamir 是法國國家科學研究中心 (CNRS) 的研究主任。他於1990年在格勒諾布爾獲得機械學位，1992年在圖盧茲獲得航空電子學位。他於1995年在格勒諾布爾科技學院獲得非線性模型預測控制的博士學位。他曾擔任格勒諾布爾-阿爾卑斯大學控制系的非線性系統與複雜性研究小組負責人。他的主要研究主題包括非線性模型預測控制、非線性移動視野估計器以及工程設備中的盲異常檢測。他曾是國際自動控制聯盟 (IFAC) 非線性系統技術委員會的成員，以及IEEE會議編輯委員會的成員，並擔任《IEEE自動控制學報》（IEEE Transactions on Automatic Control）的副編輯（2008-2021）。他於2006年在格勒諾布爾組織了第一屆針對快速系統的非線性模型預測控制 (NMPC) IFAC研討會。他是初創公司Amiral-Technologies的共同創辦人和科學顧問，該公司專注於工業預測維護和診斷的AI算法，並且是2017年GE數位工業挑戰賽的獲獎者。

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