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.
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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
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Offers technical and practical knowledge on control and estimation methods for the optimized functioning of autonomous electric vehicles’ systems, particularly path tracking, electric motors, and power electronics
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Proposes solutions for path following and localization problems of AGVs, USVs, AUVs, and UAVs, as well as solutions for the associated power supply and power management problems Targets jointly at improved performance for the autonomous navigation system and at optimality for the power management and electric traction system of robotized electric vehicles Presents nonlinear control, traction, and propulsion methods which ensure that minimization of energy consumption by autonomous electric vehicles is achieved under a zero-carbon imprint Is accompanied by audiovisual material explaining the contents of the individual sections of the monograph
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Produktdetaljer

ISBN
9780443288548
Publisert
2025-03-01
Utgiver
Vendor
Elsevier - Health Sciences Division
Høyde
229 mm
Bredde
152 mm
Aldersnivå
P, 06
Språk
Product language
Engelsk
Format
Product format
Heftet
Antall sider
400

Forfatter

Om bidragsyterne

Dr. Gerasimos Rigatos is Research Director of the Industrial Systems Institute, Greece, specializing in nonlinear control, nonlinear estimation, and fault diagnosis for complex dynamical systems. He has held visiting professor positions at various academic institutes and is a senior member of IEEE. He received his PhD from the National Technical University of Athens in 2000 and serves as an editor for the Journal of Information Sciences, the Journal of Advanced Robotic Systems, and the Journal of Electrified Vehicles. Dr. Masoud Abbaszadeh obtained his PhD in Electrical and Computer Engineering from the University of Alberta, Canada, in 2008. He has extensive experience in control systems and cyber-physical security, working at the Maplesoft–Toyota joint research team and GE Research Center. He is currently a Principal Engineer and Technical Leader of GE, while also holding an adjunct professor position at Rensselaer Polytechnic Institute. He is an associate editor of IEEE Transactions on Control Systems Technology and has over 60 patents credited to his name. Dr. Pierluigi Siano is a Professor and the Scientific Director of the Smart Grids and Smart Cities Laboratory at the University of Salerno, Italy. His research focuses on smart grids, energy management, and power systems planning. He received his PhD from the University of Salerno in 2006 and is a distinguished visiting professor at the University of Johannesburg, South Africa. He has served as the Chair of the IES Technical Committee on Smart Grids. He is an Editor for the Power & Energy Society Section of IEEE Access, IEEE Transactions on Power Systems, IEEE Transactions on Industrial Informatics, IEEE Transactions on Industrial Electronics, and IEEE Systems. 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.