Robust and Fault-Tolerant Control

​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

“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)

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