Autonomous Electric Vehicles - Nonlinear Control, Traction, and Propulsion

Autonomous Electric Vehicles explores cutting-edge technologies revolutionizing transportation and city navigation. Novel solutions to the control problem of the complex nonlinear dynamics of robotized electric vehicles are developed and tested. The new control methods are free of shortcomings met in control schemes which are based on diffeomorphisms and global linearization (complicated changes of state variables, forward and backwards state-space transformations, singularities). It is shown that such methods can be used in the steering and traction system of several types of robotized electric vehicles without needing to transform the state-space model of these systems into equivalent linearized forms. It is also shown that the new control methods can be implemented in a computationally simple manner and are also followed by global stability proofs.

Table des matières

Part I. Control and estimation of robotized vehicles’ dynamics and kinematics
1. Nonlinear optimal control and Lie algebra-based control
2. Flatness-based control in successive loops for complex nonlinear dynamical systems
3. Nonlinear optimal control for car-like front-wheel steered autonomous ground vehicles
4. Nonlinear optimal control for skid-steered autonomous ground vehicles
5. Flatness-based control in successive loops for 3-DOF unmanned surface vessels
6. Flatness-based control in successive loops for 3-DOF autonomous underwater vessels
7. Flatness-based control in successive loops for 6-DOF autonomous underwater vessels
8. Flatness-based control in successive loops for 6-DOF autonomous quadrotors
9. Flatness-based control in successive loops for 6-DOF autonomous octocopters
10. Nonlinear optimal control for 6-DOF tilt rotor autonomous quadrotors
11. Flatness-based adaptive neurofuzzy control of the four-wheel autonomous ground vehicles
12. H-infinity adaptive neurofuzzy control of the four-wheel autonomous ground vehicles
13. Fault diagnosis for four-wheel autonomous ground vehicles

Part II. Control and estimation of electric autonomous vehicles’ traction
14. Flatness-based control in successive loops for VSI-fed three-phase permanent magnet synchronous motors
15. Flatness-based control in successive loops for VSI-fed three-phase induction motors
16. Flatness-based control in successive loops and nonlinear optimal control for five-phase permanent magnet synchronous motors
17. Flatness-based control in successive loops for VSI-fed six-phase asynchronous motors
18. Flatness-based control in successive lops for nine-phase permanent magnet synchronous motors
19. Flatness-based control in successive loops of a vehicle’s clutch with actuation for permanent magnet linear synchronous motors
20. Flatness-based control in successive loops for electrohydraulic actuators
21. Flatness-based control in successive loops for electropneumatic actuators
22. Flatness-based adaptive neurofuzzy control of three-phase permanent magnet synchronous motors
23. H-infinity adaptive neurofuzzy control of three-phase permanent magnet synchronous motors
24. Fault diagnosis of a hybrid electric vehicle’s powertrain

A propos de l'auteur

Dr. Gerasimos Rigatos is currently a Research Director (Researcher Grade A') at the Industrial Systems Institute, Greece. He obtained his Ph.D. from the National Technical University of Athens (NTUA), Greece, in 2000, and was subsequently a post-doctoral researcher at IRISA-INRIA, Rennes, France. He is a Senior Member of IEEE, and a Member and CEng of IET. Dr. Rigatos has led several research cooperation agreements and projects with accredited results in the areas of nonlinear control, nonlinear filtering, and control of distributed parameter systems, and his results appear in 12 research monographs and in several journal articles. He is first author of 150 journal articles, receiving over 3,400 citations (Scopus), and is an Editor of the Journal of Information Sciences, the Journal of Advanced Robotic Systems, the SAE Journal of Electrified Vehicles, and the Journal of Power Electronics and Drives. He has held visiting professor positions at several universities in Europe.
Dr. Masoud Abbaszadeh is currently a Principal Research Engineer at the GE Vernova Research Center, NY, USA. He received his Ph.D. in Electrical and Computer Engineering from the University of Alberta, Edmonton, Canada, in 2008. From 2008 to 2011, he was a Research Engineer with Maplesoft, in Ontario, Canada, and from 2011 to 2013, he was a Senior Research Engineer at United Technologies Research Center, CT, USA, working on advanced control systems, and complex systems modelling and simulation. His research interests include estimation and detection theory, robust and nonlinear control, and machine learning with applications in cyber-physical security and resilience and autonomous systems. Dr. Abbaszadeh has authored over 170 peer-reviewed papers and 9 book chapters, holds 42 issued US patents, and has published four books. He is an Associate Editor of IEEE Transactions on Control Systems Technology, and a member of the IEEE CSS Conference Editorial Board.
Dr. Pierluigi Siano is a Professor and Scientific Director of the Smart Grids and Smart Cities Laboratory with the Department of Management and Innovation Systems, at the University of Salerno, Italy. He received his Ph.D. degree from the University of Salerno in 2006. Since 2021 he has been a Distinguished Visiting Professor in the Department of Electrical and Electronic Engineering Science, University of Johannesburg, South Africa. His research activities are centred on demand response, energy management, integration of distributed energy resources in smart grids, electricity markets, and planning and management of power systems. Prof. Siano has co-authored more than 680 articles, with 15,240 citations (Scopus), and was a Web of Science Highly Cited Researcher in Engineering in 2019, 2020, and 2021. He is Editor for the Power and Energy Society Section of IEEE Access and several other IEEE publications, and was previously Chair of the IES TC on Smart Grids.
Dr. Patrice Wira received his PhD in Electrical Engineering from Université de Haute Alsace, France, in 2002. He is a Professor at the Institut de Recherche en Informatique, Mathématiques, Automatique et Signal, Université de Haute Alsace. He specializes in artificial neural networks and adaptive control systems and their applications to power electronics. He is a senior member of IEEE and serves as an associate editor for the Energy section of the Heliyon journal (Cell Press). His research interests include control and machine learning for electric power systems and power electronics.

Détails du produit

• Auteurs: Masoud Abbaszadeh, Gerasimos Rigatos, Pierluigi Siano, Siano Pierluigi, Patrice Wira
• Edition: Elsevier
• Langues: Anglais
• Format d'édition: Livre de poche
• Sortie: 16.06.2025
• EAN: 9780443288548
• ISBN: 978-0-443-28854-8
• Poids: 450 g
• Catégories: Sciences naturelles, médecine, informatique, technique > Technique > Machines, ingénierie; Robotics, Artificial Intelligence, TECHNOLOGY & ENGINEERING / Mechanical, TECHNOLOGY & ENGINEERING / Automotive, TECHNOLOGY & ENGINEERING / Robotics, TECHNOLOGY & ENGINEERING / Electrical, Electrical Engineering, Automotive technology and trades, Mechanical Engineering, Automotive technology & trades, COMPUTERS / Artificial Intelligence / General, Intelligent and automated transport system technology, Electric vehicles / alternative fuel vehicles