Robust and Fault-Tolerant Control

Neural-Network-Based Solutions

AvKrzysztof Patan

Inbunden, Engelska, 2019

Del 197 i serien Studies in Systems, Decision and Control

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Beskrivning

Robust and Fault-Tolerant Control proposes novel automatic control strategies for nonlinear systems developed by means of artificial neural networks and pays special attention to robust and fault-tolerant approaches. The book discusses robustness and fault tolerance in the context of model predictive control, fault accommodation and reconfiguration, and iterative learning control strategies. Expanding on its theoretical deliberations the monograph includes many case studies demonstrating how the proposed approaches work in practice. The most important features of the book include:a comprehensive review of neural network architectures with possible applications in system modelling and control;a concise introduction to robust and fault-tolerant control;step-by-step presentation of the control approaches proposed;an abundance of case studies illustrating the important steps in designing robust and fault-tolerant control; anda large number of figures and tables facilitating the performance analysis of the control approaches described.The material presented in this book will be useful for researchers and engineers who wish to avoid spending excessive time in searching neural-network-based control solutions. It is written for electrical, computer science and automatic control engineers interested in control theory and their applications. This monograph will also interest postgraduate students engaged in self-study of nonlinear robust and fault-tolerant control.

Produktinformation

Utgivningsdatum:2019-03-26
Mått:155 x 235 x 19 mm
Vikt:530 g
Format:Inbunden
Språk:Engelska
Serie:Studies in Systems, Decision and Control
Antal sidor:209
Förlag:Springer Nature Switzerland AG
ISBN:9783030118686

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Mer om författaren

Krzysztof Patan was born on 26 February 1971 in Zielona Góra, Poland. He received the Ph.D. degree in Machine Design and Exploitation from Warsaw University of Technology, Poland, in 2000 and the D.Sc. degree in Electrical Engineering from University of Zielona Góra, Poland, in 2009. In 2001, he was with the University of Genova, Genova, Italy, as a Research Fellow. Currently, he is an Associate Professor with the Institute of Control and Computation Engineering, University of Zielona Góra, Poland. His research interests include artificial neural networks, and their application to modelling and identification of nonlinear systems, fault detection and diagnosis and optimization techniques and nonlinear control. Professor Patan has taken part in the realization of a number of research projects sponsored by the Ministry of Science and Higher Education in Poland and since 1997 by the European Commission: INCOCopernicus on Integration of quantitative and qualitativefault diagnosis methods within the framework of industrial application (1997–1999), the 5th FP EU RTN on Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems (DAMADICS) (2000–2004). Krzysztof Patan has published more than 135 papers in international journals and conference proceedings. He is the author of the monograph Artificial Neural Networks for the Modelling and Fault Diagnosis of Technical Processes published by Springer in 2008. In 2011 he was awarded Engineering Sciences Award by the Polish Academy of Sciences, Division Four. In 2018 he served as co-editor of the 10th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2018 held in Warsaw, Poland in 29-31 August 2018. In 2018, he was awarded by the President off the Republic of Poland with the Silver Medal for Long Service

Recensioner i media

“This monograph presents some ideas, concepts and new results in the field of robust fault-tolerant control. … This monograph opens up new possibilities for many researchers in this field to approach the front line of research.” (Anatoly Martynyuk, zbMATH 1422.93002, 2019)

Innehållsförteckning

Introduction.- Neural Networks.- Robust and Fault-Tolerant Control.- Model Predictive Control.- Control Reconfiguration.- Iterative Learning Control.- Concluding Remarks and Further Research Directions.