Nature-Inspired Optimization Algorithms for Fuzzy Controlled Servo Systems

Häftad, Engelska, 2019

1 226 kr

Beställningsvara. Skickas inom 7-10 vardagar. Fri frakt över 249 kr.

Beskrivning

Nature-inspired Optimization Algorithms for Fuzzy Controlled Servo Systems explains fuzzy control in servo systems in a way that doesn't require any solid mathematical prerequisite. Analysis and design methodologies are covered, along with specific applications to servo systems and representative case studies. The theoretical approaches presented throughout the book are validated by the illustration of digital simulation and real-time experimental results. This book is a great resource for a wide variety of readers, including graduate students, engineers (designers, practitioners and researchers), and everyone who faces challenging control problems.

Merges classical and modern approaches to fuzzy control
Presents, in a unified structure, the essential aspects regarding fuzzy control in servo systems
Explains notions of fuzzy set theory and fuzzy control to readers with limited experience

Produktinformation

Utgivningsdatum:2019-04-29
Mått:152 x 229 x undefined mm
Vikt:290 g
Format:Häftad
Språk:Engelska
Antal sidor:148
Förlag:Elsevier Science
ISBN:9780128163580

Utforska kategorier

Maskinteknik och material inom Naturvetenskap och teknik

Mer om författaren

Radu-Emil Precup received the Dipl.Ing. (Hons.) degree in automation and computers from the "Traian Vuia" Polytechnic Institute of Timisoara, Timisoara, Romania, in 1987, the Diploma in mathematics from the West University of Timisoara, Timisoara, in 1993, and the Ph.D. degree in automatic systems from the "Politehnica" University of Timisoara, Timisoara, in 1996.He was the recipient of the Elsevier Scopus Award for Excellence in Global Contribution (2017), the "Grigore Moisil" Prize from the Romanian Academy, two times, in 2005 and 2016, for his contribution on fuzzy control and the optimization of fuzzy systems, the Spiru Haret Award from the National Grand Lodge of Romania in partnership with the Romanian Academy in 2016 for education, environment and IT, the Excellency Diploma of the International Conference on Automation, Quality & Testing, Robotics AQTR 2004 (THETA 14, Cluj-Napoca, Romania), two Best Paper Awards in the Intelligent Control Area of the 2008 Conference on Human System Interaction HSI 2008, Krakow (Poland), the Best Paper Award of 16th Online World Conference on Soft Computing in Industrial Applications WSC16 (Loughborough University, UK) in 2011, the Certificate of Appreciation for the Best Paper in the Session TT07 1 Control Theory of 39th Annual Conference of the IEEE Industrial Electronics Society IECON 2013 (Vienna, Austria), a Best Paper Nomination at 12th International Conference on Informatics in Control, Automation and Robotics ICINCO 2015 (Colmar, France), and was listed as one of the top 10 researchers in Artificial Intelligence and Automation (according to IIoT World as of July 2017). Radu-Codrut David received the Dipl.Ing. degree in systems and computer engineering, the M.Sc. degree in automatic systems, and the Ph.D. degree in systems engineering from the Politehnica University of Timisoara, Timisoara, Romania, in 2006, 2008, and 2015, respectively.He is the co-author of more than 30 papers published in scientific journals, refereed conference proceedings, and as contributions to books. His research interests include control structures and algorithms with focus on fuzzy control and nature-inspired optimization.Dr. David is a Member of the Technical Committee on Soft Computing of the Institute of Electrical and Electronics Engineers Systems, Man, and Cybernetics Society.He was the recipient of "Grigore Moisil" Prize from the Romanian Academy in 2016 for his contribution on the optimization of fuzzy systems, and the Best Paper Award of 16th Online World Conference on Soft Computing in Industrial Applications WSC16 (Loughborough University, UK) in 2011.

Innehållsförteckning

1. Introduction2. Nature-inspired algorithms for the optimal tuning of fuzzy controllers3. Adaptive nature-inspired algorithms for the optimal tuning of fuzzy controllers4. Hybrid nature-inspired algorithms for the optimal tuning of fuzzy controllers5. Conclusions