Controller Tuning Optimization Methods for Multi-constraints and Nonlinear Systems : A Metaheuristic Approach

Blondin, Maude Josée

ISBN 10: 3030645401 / ISBN 13: 9783030645403

Published by Springer, 2021

About this Item

Seller Inventory # 43502176-n

Bibliographic Details

• Title: Controller Tuning Optimization Methods for Multi-constraints and Nonlinear Systems : A Metaheuristic Approach
• Publisher: Springer
• Publication Date: 2021
• Language: English
• ISBN 10: 3030645401
• ISBN 13: 9783030645403
• Binding: Soft cover
• Condition: New

About this title

Synopsis

This book covers controller tuning techniques from conventional to new optimization methods for diverse control engineering applications. Classical controller tuning approaches are presented with real-world challenges faced in control engineering. Current developments in applying optimization techniques to controller tuning are explained. Case studies of optimization algorithms applied to controller tuning dealing with nonlinearities and limitations like the inverted pendulum and the automatic voltage regulator are presented with performance comparisons. Students and researchers in engineering and optimization interested in optimization methods for controller tuning will utilize this book to apply optimization algorithms to controller tuning, to choose the most suitable optimization algorithm for a specific application, and to develop new optimization techniques for controller tuning.

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. Afterward, 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 and soft computing methods for multiagent systems. These algorithms are used in many applications ranging from managing energy to military uses to swarm robotics.