Computational Intelligence and Optimization Methods for Control Engineering

Maude Blondin graduated with a PhD in Electrical Engineering from the Université du Québec à Trois-Rivières, where she obtained the prestigious Vanier Canada Graduate Scholarship. During her PhD, she did a six-month internship at the Universidad Politecnica de Valencia, under the supervision of Javier Sanchis Saez. She moved to Florida to work in close collaboration with her PhD co-advisor, Panos M. Pardalos. Her doctoral research was on computational intelligence methods, and soft computing techniques applied to control engineering. Dr. Blondin is currently a postdoctoral researcher in the mechanical and aerospace engineering department at the University of Florida. She is expanding her research interests to the use of multiobjective optimization applied to multiagent control strategies.

Foreword.- Preface. - Control engineering from classical to intelligent control theory - An overview (Blondin, Sáez, Pardalos).- Main metaheuristics used for the optimization of the control of the complex systems (Borne, Gharbi).-  Optimal controller parameter tuning from multi/many objective optimization algorithms (Altinoz).- Fuzzy and neuro-fuzzy control for smart structures (Tairidis, Stavroulakis).- Computational intelligence in the desalination industry (Cabrera, Carta).- Control of complex biological systems utilizing the neural network predictor (Bamgbose, Li, Qian).- A real-time big-data control-theoretical framework for cyber-physical-human systems (Gusrialdi, Xi, Qu, Simaan).- Distributed optimization based control on the example of microgrids (Braun, Sauerteig, Worthmann).- Coherency estimation in power systems: A Koopman operator approach (Chamorro, Ordonez, Peng, Gonzalez-Longatt, Sood).- Appliance identification through non-intrusive load monitoring in residences (Gogos, Georgiou).- Management suggestions for process control of semiconductor  manufacturing: An operations research and data science perspective (Khakifirooz, Fathi, Chien, Pardalos).- Feedback control algorithms for the dissipation of traffic waves with autonomous vehicles (Monache, Liard, Rat, Stern, Bhadani, Seibold, Sprinkle, Work, Piccoli).- Disturbance rejection run-to-run controller for semiconductor manufacturing (Khakifirooz, Fathi, Pardalos).- Energy management improvement based on fleet learning for hybrid electric buses (López-Ibarra, Herrera, Milo, Gaztañaga, Camblong).