Optimal Event-Triggered Control Using Adaptive Dynamic Programming
Jagannathan, Sarangapani, Narayanan, Vignesh, Sahoo, Avimanyu
- 出版商: CRC
- 出版日期: 2024-06-21
- 售價: $4,030
- 貴賓價: 9.5 折 $3,829
- 語言: 英文
- 頁數: 333
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 1032468653
- ISBN-13: 9781032468655
海外代購書籍(需單獨結帳)
相關主題
商品描述
Optimal Event-triggered Control using Adaptive Dynamic Programming discusses event triggered controller design which includes optimal control and event sampling design for linear and nonlinear dynamic systems including networked control systems (NCS) when the system dynamics are both known and uncertain. The NCS are a first step to realize cyber-physical systems (CPS) or industry 4.0 vision. The authors apply several powerful modern control techniques to the design of event-triggered controllers and derive event-trigger condition and demonstrate closed-loop stability. Detailed derivations, rigorous stability proofs, computer simulation examples, and downloadable MATLAB(R) codes are included for each case.
The book begins by providing background on linear and nonlinear systems, NCS, networked imperfections, distributed systems, adaptive dynamic programming and optimal control, stability theory, and optimal adaptive event-triggered controller design in continuous-time and discrete-time for linear, nonlinear and distributed systems. It lays the foundation for reinforcement learning-based optimal adaptive controller use for infinite horizons. The text then:
- Introduces event triggered control of linear and nonlinear systems, describing the design of adaptive controllers for them
- Presents neural network-based optimal adaptive control and game theoretic formulation of linear and nonlinear systems enclosed by a communication network
- Addresses the stochastic optimal control of linear and nonlinear NCS by using neuro dynamic programming
- Explores optimal adaptive design for nonlinear two-player zero-sum games under communication constraints to solve optimal policy and event trigger condition
- Treats an event-sampled distributed linear and nonlinear systems to minimize transmission of state and control signals within the feedback loop via the communication network
- Covers several examples along the way and provides applications of event triggered control of robot manipulators, UAV and distributed joint optimal network scheduling and control design for wireless NCS/CPS in order to realize industry 4.0 vision
An ideal textbook for senior undergraduate students, graduate students, university researchers, and practicing engineers, Optimal Event Triggered Control Design using Adaptive Dynamic Programming instills a solid understanding of neural network-based optimal controllers under event-sampling and how to build them so as to attain CPS or Industry 4.0 vision.
商品描述(中文翻譯)
《適應性動態規劃的最佳事件觸發控制》討論了事件觸發控制器的設計，包括線性和非線性動態系統的最佳控制和事件採樣設計，包括在系統動態已知和不確定的情況下的網絡控制系統（NCS）。NCS是實現物聯網系統（CPS）或工業4.0的第一步。作者應用了幾種強大的現代控制技術來設計事件觸發控制器，並推導出事件觸發條件並證明了閉環穩定性。每個案例都包括詳細的推導、嚴格的穩定性證明、計算機模擬示例和可下載的MATLAB(R)代碼。
本書首先介紹了線性和非線性系統、NCS、網絡不完美性、分佈式系統、適應性動態規劃和最佳控制、穩定性理論以及連續時間和離散時間的線性、非線性和分佈式系統的最佳適應性事件觸發控制器設計。它為基於強化學習的最佳適應性控制器在無窮時間範圍內的使用奠定了基礎。接著，本書介紹了線性和非線性系統的事件觸發控制，描述了為其設計適應性控制器的方法。它還介紹了基於神經網絡的最佳適應性控制和線性、非線性系統的博弈論形式，以及使用神經動態規劃來處理線性和非線性NCS的隨機最佳控制。本書還探討了在通信限制下解決非線性雙人零和遊戲的最佳適應性設計，以求得最佳策略和事件觸發條件。此外，本書還涵蓋了事件採樣的分佈式線性和非線性系統，以最小化通過通信網絡在反饋環中傳輸狀態和控制信號。在整個過程中，本書提供了多個示例，並應用事件觸發控制於機器人操縱器、無人機和分佈式聯合最佳網絡調度和控制設計，以實現無線NCS/CPS的工業4.0視覺。
《適應性動態規劃的最佳事件觸發控制設計》是高年級本科生、研究生、大學研究人員和實踐工程師的理想教材，它培養了對基於神經網絡的事件採樣下最佳控制器的深入理解，以及如何構建這些控制器以實現CPS或工業4.0的視覺。
作者簡介
Dr. Sarangapani Jagannathan is a Curator's Distinguished Professor and Rutledge-Emerson chair of Electrical and Computer Engineering at the Missouri University of Science and Technology (former University of Missouri-Rolla). He has a joint Professor appointment in the Department of Computer Science. He served as a Director for the NSF Industry/University Cooperative Research Center on Intelligent Maintenance Systems for 13 years. His research interests include learning, adaptation and control, secure human-cyber-physical systems, prognostics, and autonomous systems/robotics. Prior to his Missouri S&T appointment, he served as a faculty at University of Texas at San Antonio and as a staff engineer at Caterpillar, Peoria.
He has coauthored over 500 refereed IEEE Transaction/journal and conference articles, written 18 book chapters, authored/co-edited 6 books, received 21 US patents and one patent defense publication. He delivered around 30 plenary and keynote talks in various international conferences and supervised to graduation 33 doctoral and 31 M.S thesis students. He was a co-editor for the IET book series on control from 2010 until 2013 and served on many editorial boards including IEEE Systems, Man and Cybernetics, and has been on organizing committees of several IEEE Conferences. He is currently an associate editor for IEEE Transactions on Neural Networks and Learning Systems and others.
He received many awards including the 2020 Best Associate Editor Award, 2018 IEEE CSS Transition to Practice Award, 2007 Boeing Pride Achievement Award, 2001 Caterpillar Research Excellence Award, 2021 University of Missouri Presidential Award for sustained career excellence, 2001 University of Texas Presidential Award for early career excellence, and 2000 NSF Career Award. He also received several faculty excellence and teaching excellence and commendation awards. As part of his NSF I/UCRC, he transitioned many technologies and software products to industrial entities saving millions of dollars. He is a Fellow of the IEEE, National Academy of Inventors, and Institute of Measurement and Control, UK, Institution of Engineering and Technology (IET), UK and Asia-Pacific Artificial Intelligence Association.
Dr. Vignesh Narayanan is an Assistant Professor in the AI institute and the Department of Computer Science and Engineering at University of South Carolina (USC), Columbia. He is also affiliated with the Carolina Autism and Neurodevelopment research center at USC. His research interests include dynamical systems and networks, artificial intelligence, data science, learning theory, and computational neuroscience.
He received his B.Tech. Electrical and electronics engineering and M. Tech. Electrical engineering degrees from SASTRA University, Thanjavur, and the National Institute of Technology, Kurukshetra, India, respectively, in 2012 and 2014, and his Ph.D. degree from Missouri University of Science and Technology, Rolla, MO in 2017. He was a post-doctoral research associate at Washington University in St. Louis, before joining the AI institute of USC.
Avimanyu Sahoo received his Ph.D. in Electrical Engineering from Missouri University of Science and Technology, Rolla, MO, USA, in 2015 and a Master of Technology (MTech) from the Indian Institute of Technology (BHU), Varanasi, India, in 2011. He is currently an Assistant Professor in the Electrical and Computer Engineering Department at the University of Alabama in Huntsville (UAH), AL. Before joining UAH, Dr. Sahoo was an Associate Professor in the Division of Engineering Technology at Oklahoma State University, Stillwater, OK.
Dr. Sahoo's research interests include learning-based control and its applications in lithium-ion battery pack modeling, diagnostics, prognostics, cyber-physical systems (CPS), and electric machinery health monitoring. Currently, his research focuses on developing intelligent battery management systems (BMS) for lithium-ion battery packs used onboard electric vehicles, computation, and communication-efficient distributed intelligent control schemes for cyber-physical systems using approximate dynamic programming, reinforcement learning, and distributed adaptive state estimation. He has published over 45 journal and conference articles, including IEEE Transactions on Neural Networks and Learning Systems, Cybernetics, and Industrial Electronics. He is also an Associate Editor in IEEE Transactions on Neural Networks and Learning Systems and Frontiers in Control Engineering: Nonlinear Control.
作者簡介(中文翻譯)
Dr. Sarangapani Jagannathan是密蘇里科學與技術大學（前身為密蘇里羅拉大學）電氣與電腦工程系的Curator's Distinguished Professor和Rutledge-Emerson講座教授。他在計算機科學系擔任聯合教授職務。他曾擔任國家科學基金會智能維護系統產學合作研究中心主任長達13年。他的研究興趣包括學習、適應和控制、安全的人機物聯網系統、預測和自主系統/機器人技術。在加入密蘇里科學與技術大學之前，他曾在聖安東尼奧德州大學擔任教職，並在卡特彼勒公司皮奧里亞分部擔任工程師。
他共同撰寫了500多篇經過同行評審的IEEE交易/期刊和會議論文，撰寫了18篇專書章節，撰寫/共同編輯了6本書，獲得了21項美國專利和一項專利辯護出版物。他在各種國際會議上發表了約30次主題演講，並指導了33名博士和31名碩士研究生。他曾擔任2010年至2013年控制領域IET書籍系列的共同編輯，並擔任IEEE Systems, Man and Cybernetics等多個編輯委員會的成員。他目前是IEEE Transactions on Neural Networks and Learning Systems等期刊的副編輯。
他獲得了許多獎項，包括2020年最佳副編輯獎、2018年IEEE CSS實踐轉化獎、2007年波音自豪成就獎、2001年卡特彼勒研究卓越獎、2021年密蘇里大學總統獎（持續職業卓越獎）、2001年德州大學總統獎（早期職業卓越獎）和2000年NSF職業獎。他還獲得了多項教學和卓越教學獎。作為NSF I/UCRC的一部分，他將許多技術和軟件產品轉移到工業實體，節省了數百萬美元。他是IEEE、國家發明家學會、英國測量和控制學會、英國工程技術學會（IET）和亞太人工智能協會的會士。
Dr. Vignesh Narayanan是南卡羅來納大學（USC）人工智能研究所和計算機科學與工程系的助理教授。他還與USC的卡羅來納自閉症和神經發育研究中心有聯繫。他的研究興趣包括動態系統和網絡、人工智能、數據科學、學習理論和計算神經科學。
他於2012年和2014年分別在印度SASTRA大學和國立技術學院獲得電氣與電子工程學士學位，並於2017年在密蘇里科學與技術大學獲得博士學位。在加入USC的人工智能研究所之前，他曾在華盛頓大學聖路易斯分校擔任博士後研究助理。
Avimanyu Sahoo於2015年在密蘇里科學與技術大學獲得電氣工程博士學位，並於2011年在印度理工學院（BHU）獲得技術碩士學位。他目前是阿拉巴馬大學亨茨維爾分校（UAH）電氣與電腦工程系的助理教授。在加入UAH之前，Sahoo博士是俄克拉荷馬州立大學工程技術學部的副教授。
Sahoo博士的研究興趣包括基於學習的控制及其在鋰離子電池組建模、診斷、預測、人機物聯網系統和電機設備健康監測方面的應用。目前，他的研究重點是開發智能電池管理系統（BMS）。
