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9783031647437

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Juan Moreno Nadales received his BSc in Electronic Engineering from the University of Cordoba in 2017. He moved to the University of Seville where he received his MSc in Control and System Engineering in 2018, specializing in model predictive control, and his MSc in Microelectronic Design in 2019, specializing in the implementation of bio-inspired algorithms on configurable embedded platforms. He received the PhD in Control and Systems Engineering from the University of Seville in 2024, specializing in the application of robust model predictive control techniques to safe navigation management. In 2022 he carried out a research stay at Seoul National University, Republic of Korea, in collaboration with the Optimization and Control Research Lab. His main research line is the application of new predictive control strategies and optimization techniques to different fields such as logistics, power electronic systems, or intelligent air conditioning systems. From 2024, he is a post-doctoral  researcher at the Earth-Life Science Institute (ELSI) of Tokyo Institute of Technology, Japan, where he specializing in artificial life and the integration of integration of living organisms with computational entities in  shared ecosystems.  David Muñoz de la Peña was born in Badajoz, Spain, in 1978. He received the Laurea degree in telecommunication engineering in 2001, and the Ph.D. degree in automation, robotic, and telecommunication in 2005, both from the University of Seville, Spain. He spent the academic year 2003-2004 at the Systems Control Group at the Department of Information Engineering of the University of Siena. In 2006-2007, he held a post-doctoral position at the Chemical and Biomolecular Engineering Department at the University of California Los Angeles. Since 2007, he is with the Department of Automation and Systems Engineering of the University of Seville, becoming a full professor in 2017. His theoretical research interests include model predictive control, distributed systems, process control, multiparametric optimization, and machine learning. His research work has resulted in a large number of articles in leading scientific journals and conferences. Daniel Limon received the M.Eng. and Ph.D. degrees in electrical engineering from the University of Seville, Seville, Spain, in 1996 and 2002, respectively. From 1999 to 2007, he was an Assistant Professor with the Departamento de Ingeniería de Sistemas y Automática, University of Seville, from 2007 to 2017 Associate Professor and since 2017,  Full Professor in the same Department. He has been visiting researcher at the University of Cambridge and the Mitsubishi Electric Research Labs in 2016 and 2018 respectively. Dr. Limon has been a Keynote Speaker at the International Workshop on Assessment and Future Directions of Nonlinear Model Predictive Control in 2008 and Semiplenary Lecturer at the IFAC Conference on Nonlinear Model Predictive Control in 2012. He has been the Chair of the fifth IFAC Conference on Nonlinear Model Predictive Control (2015). His current research interests include model predictive control, stability and robustness analysis, tracking control , optimal operation and data-based control with application to buildings and water distribution networks, spacecraft rendevouz strategies and navigation planning. Teodoro Alamo received the M.Eng. degree in telecommunications engineering from the Polytechnic University of Madrid in 1993, and the PhD degree in telecommunications engineering from the University of Seville in 1998. He has been a Full Professor of the Department of Automation and Systems Engineering in the University of Seville since 2010. He was with the Ecole Nationale Superieure des Télécommunications (Télécom Paris), Paris, France, from September 1991 to May 1993. Part of his PhD work was done at RWTH Aachen, Germany, from June to September 1995. He has co-founded the spin-off company Optimal Performance (University of Seville). He is the author or co-author of more than 200 publications, including books, book chapters, journal articles, and conference proceedings. His current research interests include decision-making, model predictive control, data-driven methods, randomized algorithms, and optimization strategies.

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