Data-Driven Model-Free Controllers

Data-Driven Model-Free Controllers
Author :
Publisher : CRC Press
Total Pages : 408
Release :
ISBN-13 : 9781000519631
ISBN-10 : 1000519635
Rating : 4/5 (35 Downloads)

Book Synopsis Data-Driven Model-Free Controllers by : Radu-Emil Precup

Download or read book Data-Driven Model-Free Controllers written by Radu-Emil Precup and published by CRC Press. This book was released on 2021-12-27 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book categorizes the wide area of data-driven model-free controllers, reveals the exact benefits of such controllers, gives the in-depth theory and mathematical proofs behind them, and finally discusses their applications. Each chapter includes a section for presenting the theory and mathematical definitions of one of the above mentioned algorithms. The second section of each chapter is dedicated to the examples and applications of the corresponding control algorithms in practical engineering problems. This book proposes to avoid complex mathematical equations, being generic as it includes several types of data-driven model-free controllers, such as Iterative Feedback Tuning controllers, Model-Free Controllers (intelligent PID controllers), Model-Free Adaptive Controllers, model-free sliding mode controllers, hybrid model‐free and model‐free adaptive‐Virtual Reference Feedback Tuning controllers, hybrid model-free and model-free adaptive fuzzy controllers and cooperative model-free controllers. The book includes the topic of optimal model-free controllers, as well. The optimal tuning of model-free controllers is treated in the chapters that deal with Iterative Feedback Tuning and Virtual Reference Feedback Tuning. Moreover, the extension of some model-free control algorithms to the consensus and formation-tracking problem of multi-agent dynamic systems is provided. This book can be considered as a textbook for undergraduate and postgraduate students, as well as a professional reference for industrial and academic researchers, attracting the readers from both industry and academia.


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