Springer Intelligent Control and Smart Energy Management: Renewable Resources and Transportation: 181 (Springer Optimization and Its Applications, 181)

This volume aims to provide a state-of-the-art and the latest advancements in the field of intelligent control and smart energy management.  Techniques, combined with technological advances, have enabled the deployment of new operating systems in many engineering applications, especially in the domain of transport and renewable resources. The control and energy management of transportation and renewable resources are shifting towards autonomous reasoning, learning, planning and operating. As a result, these techniques, also referred to as autonomous control and energy management, will become practically ubiquitous soon. The discussions include methods, based on neural control (and others) as well as distributed and intelligent optimization.  While the theoretical concepts are detailed and explained, the techniques presented are tailored to transport and renewable resources applications, such as smart grids and automated vehicles. The reader will grasp the most important theoretical concepts as well as to fathom the challenges and needs related to timely practical applications. Additional content includes research perspectives and future direction as well as insight into the devising of techniques that will meet tomorrow’s scientific needs. This contributed volume is for researchers, graduate students, engineers and practitioners in the domains of control, energy, and transportation. From the Back Cover This volume aims to provide a state-of-the-art and the latest advancements in the field of intelligent control and smart energy management.  Techniques, combined with technological advances, have enabled the deployment of new operating systems in many engineering applications, especially in the domain of transport and renewable resources. The control and energy management of transportation and renewable resources are shifting towards autonomous reasoning, learning, planning and operating. As a result, these techniques, also referred to as autonomous control and energy management, will become practically ubiquitous soon. The discussions include methods, based on neural control (and others) as well as distributed and intelligent optimization.  While the theoretical concepts are detailed and explained, the techniques presented are tailored to transport and renewable resources applications, such as smart grids and automated vehicles. The reader will grasp the most important theoretical concepts as well as to fathom the challenges and needs related to timely practical applications. Additional content includes research perspectives and future direction as well as insight into the devising of techniques that will meet tomorrow’s scientific needs. This contributed volume is for researchers, graduate students, engineers and practitioners in the domains of control, energy, and transportation. About the Author Maude Blondin is an assistant professor at the Université de Sherbrooke in Canada. She graduated with a Ph.D. in Electrical Engineering from the Université du Québec à Trois-Rivières,  where she obtained the prestigious Vanier Canada Graduate Scholarship. Her doctoral research was on computational intelligence methods and soft computing techniques applied to control engineering. After her Ph.D., Dr. Blondin did postdoctoral research in the mechanical and aerospace engineering department at the University of Florida. She expanded her research interests to multiobjective optimization applied to multiagent control strategies. Her current research is driven by developing distributed multiobjective optimization algorithms based on exploring the Pareto Front, soft computing methods, and swarm intelligence for multiagent systems. These algorithms apply to many applications ranging from energy management to military uses. Dr. Blondin is a Associate Editor of SN Operations Research Forum, has co-edited two contributed books, and served as a reviewers for many recognized journals. She has p