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Autonomous Electric Vehicles
Nonlinear Control, Traction, and Propulsion
- 1st Edition - March 1, 2025
- Authors: Gerasimos Rigatos, Pierluigi Siano, Masoud Abbaszadeh, Patrice Wira
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 2 8 8 5 4 - 8
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 2 8 8 5 5 - 5
The integration of ground-breaking technologies, such as next-generation batteries and AI-powered systems, promises to reshape the way we commute, transport goods, and navigate our… Read more
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Request a sales quoteThe integration of ground-breaking technologies, such as next-generation batteries and AI-powered systems, promises to reshape the way we commute, transport goods, and navigate our cities. Autonomous Electric Vehicles: Nonlinear Control, Traction, and Propulsion offers sought-after, specialized know-how on robotized electric vehicles (ground, surface, underwater, aerial). The book builds on theoretical fundamentals to then comprehensively cover the very latest research advances in nonlinear control, estimation, and fault diagnosis for autonomous navigation and electric traction systems. Part I investigates nonlinear optimal control and estimation of a specific class of vehicle per chapter, while part II control and dynamic modeling of a specific type of electric motor per chapter. Furthermore, the methodological analysis conducted is not constrained by the shortcomings of global linearization-based control algorithms, is computationally easy to implement, and is also corroborated by global robustness and stability proofs. Case studies and other practical application discussions exemplify these methods’ potential prospects if adopted at commercial scale. Readers from a wide range of related disciplines will benefit from the structured, modular approach of the volume, which was written by a group of experts with backgrounds both in academia and industry, whose aim is also to contribute transformative solutions to accelerate the global low-carbon power transition as well as smart energy management systems for the continuing shift to renewables.
- Investigates control and estimation methods which enable key developments in vehicle engineering (ground, surface, underwater, aerial) that are of interest on a global scale
- Presents real-life case studies and application examples, facilitating understanding of the related challenges for the maturation of scalable solutions matching the needs of the industry
- Accompanied by a companion site which includes videos of simulation tests of the control and estimation methods discussed
Postgraduate students, researchers, and academics in vehicle engineering (ground, surface, underwater, aerial), intelligent transport engineering, robotics and mechatronics, electrical and computer engineering, as well as electric power systems and power electronics
Part I. Control and estimation of robotized vehicles’ dynamics and kinematics
1. Nonlinear optimal control and Lie algebra-based control
1.1 Nonlinear optimal control
1.2 Lie algebra-based control
2. Flatness-based control in successive loops for complex nonlinear dynamical systems
2.1 Flatness-based control with transformation into canonical forms
2.2. Flatness-based control implemented in successive loops
3. Nonlinear optimal control for car-like front-wheel steered autonomous ground vehicles
3.1 Kinematics/dynamics of car-like four-wheel autonomous ground vehicles
3.2 Control of the car-like vehicle using global linearization transformations
3.3 Nonlinear optimal control of car-like vehicles
3.4 Tests on path following
4. Nonlinear optimal control for skid-steered autonomous ground vehicles
4.1 Kinematics/dynamics of skid-steered autonomous ground vehicles
4.2 Control of skid-steered vehicles using global linearization transformations
4.3 Nonlinear optimal control of skid-steered autonomous ground vehicles
4.4 Tests on path following
5. Flatness-based control in successive loops for 3-DOF unmanned surface vessels
5.1 Dynamic model of a 3-DOF unmanned surface vessel
5.2 Flatness-based control in cascading loops for the 3-DOF USVs
5.3 Tests on path following
6. Flatness-based control in successive loops for 3-DOF autonomous underwater vessels
6.1 Dynamic model of a 3-DOF autonomous underwater vessel
6.2 Flatness-based control in cascading loops for 3-DOF AUVs
6.3 Tests on path following
7. Flatness-based control in successive loops for 6-DOF autonomous underwater vessels
7.1 Dynamic model of a 6-DOF unmanned underwater vessel
7.2 Flatness-based control in cascading loops for 6-DOF AUVs
7.3 Tests on path following
8. Flatness-based control in successive loops for 6-DOF autonomous quadrotors
8.1 Dynamic model of a 6-DOF autonomous quadrotor
8.2 Flatness-based control in cascading loops for 6-DOF autonomous quadrotors
8.3 Tests on path following
9. Flatness-based control in successive loops for 6-DOF autonomous octocopters
9.1 Dynamic model of a 6-DOF autonomous octocopter
9.2 Flatness-based control in cascading loops for 6-DOF autonomous octocopters
9.3 Tests on path following
10. Nonlinear optimal control for 6-DOF tilt rotor autonomous quadrotors
10.1 Dynamic model of a 6-DOF tilt-rotor autonomous quadrotor
10.2 Nonlinear optimal control of 6-DOF autonomous quadrotors
10.3. Tests on path following
11. Flatness-based adaptive neurofuzzy control of the four-wheel autonomous ground vehicles
11.1 Global linearization of the kinematic/dynamic model of a four-wheel autonomous ground vehicle
11.2 Design of a flatness-based adaptive neurofuzzy controller for four-wheel autonomous ground vehicles
11.3 Performance tests of the flatness-based adaptive neurofuzzy controller
12. H-infinity adaptive neurofuzzy control of the four-wheel autonomous ground vehicles
12.1 Approximate linearization of a kinematic/dynamic model of a four-wheel autonomous ground vehicle
12.2 Design of an H-infinity adaptive neurofuzzy controller for four-wheel autonomous ground vehicles
12.3 Performance tests of the H-infinity adaptive neurofuzzy controller
13. Fault diagnosis for four-wheel autonomous ground vehicles
13.1 Dynamic model of a four-wheel autonomous ground vehicle
13.2 Nonlinear filtering and disturbance observers for four-wheel vehicles
13.3 Statistical fault diagnosis for four-wheel autonomous 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
14.1 Dynamic model of a VSI-fed three-phase permanent magnet synchronous motor
14.2 Flatness-based control in cascading loops for VSI-fed three-phase permanent magnet synchronous motors
14.3 Performance tests of the flatness-based controller
15. Flatness-based control in successive loops for VSI-fed three-phase induction motors
15.1 Dynamic model of a VSI-fed three-phase induction motor
15.2 Flatness-based control in successive loops for VSI-fed 3-phase induction motors
15.3. Performance tests of the flatness-based controller
16. Flatness-based control in successive loops and nonlinear optimal control for five-phase permanent magnet synchronous motors
16.1 Dynamic model of a five-phase permanent magnet synchronous motor
16.2 Flatness-based control in successive loops for five-phase permanent magnet synchronous motors
16.3. Performance tests of the flatness-based controller
16.4 Nonlinear optimal control for five-phase permanent magnet synchronous motors
16.5. Performance tests of the nonlinear optimal controller
17. Flatness-based control in successive loops for VSI-fed six-phase asynchronous motors
17.1 Dynamic model of a VSI-fed six-phase asynchronous motor
17.2 Flatness-based control in successive loops for VSI-fed six-phase asynchronous motors
17.3. Performance tests of the flatness-based controller
18. Flatness-based control in successive lops for nine-phase permanent magnet synchronous motors
18.1 Dynamic model of a nine-phase permanent magnet synchronous motor
18.2 Flatness-based control in successive loops for nine-phase permanent magnet synchronous motors
18.3. Performance tests of the flatness-based controller
19. Flatness-based control in successive loops of a vehicle’s clutch with actuation for permanent magnet linear synchronous motors
19.1 Dynamic model of a permanent magnet linear synchronous motor-driven vehicle’s clutch
19.2 Flatness-based control in successive loops for a vehicle’s clutch
19.3. Performance tests of the flatness-based controller
20. Flatness-based control in successive loops for electrohydraulic actuators
20.1 Dynamic model of an electrohydraulic actuator
20.2 Flatness-based control in successive loops for electrohydraulic actuators
20.3. Performance tests of the flatness-based controller
21. Flatness-based control in successive loops for electropneumatic actuators
21.1 Dynamic model of an electropneumatic actuator
21.2 Flatness-based control in successive loops for electropneumatic actuators
21.3. Performance tests of the flatness-based controller
22. Flatness-based adaptive neurofuzzy control of three-phase permanent magnet synchronous motors
22.1 Global linearization of the dynamic model of a three-phase permanent magnet synchronous motor
22.2 Design of a flatness-based adaptive neurofuzzy controller for three-phase permanent magnet synchronous motors
22.3 Performance tests of the flatness-based adaptive neurofuzzy controller
23. H-infinity adaptive neurofuzzy control of three-phase permanent magnet synchronous motors
23.1 Approximate linearization of the dynamic model of a three-phase permanent magnet synchronous motor
23.2 Design of an H-infinity adaptive neurofuzzy controller for three-phase permanent magnet synchronous motors
23.3 Performance tests of the H-infinity adaptive neurofuzzy controller
24. Fault diagnosis of a hybrid electric vehicle’s powertrain
24.1 Dynamic model of a hybrid electric vehicle’s powertrain
24.2 Nonlinear filtering and disturbance observers for hybrid electric vehicles’ powertrains
24.3 Statistical fault diagnosis for hybrid electric vehicles’ powertrains
1. Nonlinear optimal control and Lie algebra-based control
1.1 Nonlinear optimal control
1.2 Lie algebra-based control
2. Flatness-based control in successive loops for complex nonlinear dynamical systems
2.1 Flatness-based control with transformation into canonical forms
2.2. Flatness-based control implemented in successive loops
3. Nonlinear optimal control for car-like front-wheel steered autonomous ground vehicles
3.1 Kinematics/dynamics of car-like four-wheel autonomous ground vehicles
3.2 Control of the car-like vehicle using global linearization transformations
3.3 Nonlinear optimal control of car-like vehicles
3.4 Tests on path following
4. Nonlinear optimal control for skid-steered autonomous ground vehicles
4.1 Kinematics/dynamics of skid-steered autonomous ground vehicles
4.2 Control of skid-steered vehicles using global linearization transformations
4.3 Nonlinear optimal control of skid-steered autonomous ground vehicles
4.4 Tests on path following
5. Flatness-based control in successive loops for 3-DOF unmanned surface vessels
5.1 Dynamic model of a 3-DOF unmanned surface vessel
5.2 Flatness-based control in cascading loops for the 3-DOF USVs
5.3 Tests on path following
6. Flatness-based control in successive loops for 3-DOF autonomous underwater vessels
6.1 Dynamic model of a 3-DOF autonomous underwater vessel
6.2 Flatness-based control in cascading loops for 3-DOF AUVs
6.3 Tests on path following
7. Flatness-based control in successive loops for 6-DOF autonomous underwater vessels
7.1 Dynamic model of a 6-DOF unmanned underwater vessel
7.2 Flatness-based control in cascading loops for 6-DOF AUVs
7.3 Tests on path following
8. Flatness-based control in successive loops for 6-DOF autonomous quadrotors
8.1 Dynamic model of a 6-DOF autonomous quadrotor
8.2 Flatness-based control in cascading loops for 6-DOF autonomous quadrotors
8.3 Tests on path following
9. Flatness-based control in successive loops for 6-DOF autonomous octocopters
9.1 Dynamic model of a 6-DOF autonomous octocopter
9.2 Flatness-based control in cascading loops for 6-DOF autonomous octocopters
9.3 Tests on path following
10. Nonlinear optimal control for 6-DOF tilt rotor autonomous quadrotors
10.1 Dynamic model of a 6-DOF tilt-rotor autonomous quadrotor
10.2 Nonlinear optimal control of 6-DOF autonomous quadrotors
10.3. Tests on path following
11. Flatness-based adaptive neurofuzzy control of the four-wheel autonomous ground vehicles
11.1 Global linearization of the kinematic/dynamic model of a four-wheel autonomous ground vehicle
11.2 Design of a flatness-based adaptive neurofuzzy controller for four-wheel autonomous ground vehicles
11.3 Performance tests of the flatness-based adaptive neurofuzzy controller
12. H-infinity adaptive neurofuzzy control of the four-wheel autonomous ground vehicles
12.1 Approximate linearization of a kinematic/dynamic model of a four-wheel autonomous ground vehicle
12.2 Design of an H-infinity adaptive neurofuzzy controller for four-wheel autonomous ground vehicles
12.3 Performance tests of the H-infinity adaptive neurofuzzy controller
13. Fault diagnosis for four-wheel autonomous ground vehicles
13.1 Dynamic model of a four-wheel autonomous ground vehicle
13.2 Nonlinear filtering and disturbance observers for four-wheel vehicles
13.3 Statistical fault diagnosis for four-wheel autonomous 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
14.1 Dynamic model of a VSI-fed three-phase permanent magnet synchronous motor
14.2 Flatness-based control in cascading loops for VSI-fed three-phase permanent magnet synchronous motors
14.3 Performance tests of the flatness-based controller
15. Flatness-based control in successive loops for VSI-fed three-phase induction motors
15.1 Dynamic model of a VSI-fed three-phase induction motor
15.2 Flatness-based control in successive loops for VSI-fed 3-phase induction motors
15.3. Performance tests of the flatness-based controller
16. Flatness-based control in successive loops and nonlinear optimal control for five-phase permanent magnet synchronous motors
16.1 Dynamic model of a five-phase permanent magnet synchronous motor
16.2 Flatness-based control in successive loops for five-phase permanent magnet synchronous motors
16.3. Performance tests of the flatness-based controller
16.4 Nonlinear optimal control for five-phase permanent magnet synchronous motors
16.5. Performance tests of the nonlinear optimal controller
17. Flatness-based control in successive loops for VSI-fed six-phase asynchronous motors
17.1 Dynamic model of a VSI-fed six-phase asynchronous motor
17.2 Flatness-based control in successive loops for VSI-fed six-phase asynchronous motors
17.3. Performance tests of the flatness-based controller
18. Flatness-based control in successive lops for nine-phase permanent magnet synchronous motors
18.1 Dynamic model of a nine-phase permanent magnet synchronous motor
18.2 Flatness-based control in successive loops for nine-phase permanent magnet synchronous motors
18.3. Performance tests of the flatness-based controller
19. Flatness-based control in successive loops of a vehicle’s clutch with actuation for permanent magnet linear synchronous motors
19.1 Dynamic model of a permanent magnet linear synchronous motor-driven vehicle’s clutch
19.2 Flatness-based control in successive loops for a vehicle’s clutch
19.3. Performance tests of the flatness-based controller
20. Flatness-based control in successive loops for electrohydraulic actuators
20.1 Dynamic model of an electrohydraulic actuator
20.2 Flatness-based control in successive loops for electrohydraulic actuators
20.3. Performance tests of the flatness-based controller
21. Flatness-based control in successive loops for electropneumatic actuators
21.1 Dynamic model of an electropneumatic actuator
21.2 Flatness-based control in successive loops for electropneumatic actuators
21.3. Performance tests of the flatness-based controller
22. Flatness-based adaptive neurofuzzy control of three-phase permanent magnet synchronous motors
22.1 Global linearization of the dynamic model of a three-phase permanent magnet synchronous motor
22.2 Design of a flatness-based adaptive neurofuzzy controller for three-phase permanent magnet synchronous motors
22.3 Performance tests of the flatness-based adaptive neurofuzzy controller
23. H-infinity adaptive neurofuzzy control of three-phase permanent magnet synchronous motors
23.1 Approximate linearization of the dynamic model of a three-phase permanent magnet synchronous motor
23.2 Design of an H-infinity adaptive neurofuzzy controller for three-phase permanent magnet synchronous motors
23.3 Performance tests of the H-infinity adaptive neurofuzzy controller
24. Fault diagnosis of a hybrid electric vehicle’s powertrain
24.1 Dynamic model of a hybrid electric vehicle’s powertrain
24.2 Nonlinear filtering and disturbance observers for hybrid electric vehicles’ powertrains
24.3 Statistical fault diagnosis for hybrid electric vehicles’ powertrains
- No. of pages: 400
- Language: English
- Edition: 1
- Published: March 1, 2025
- Imprint: Elsevier
- Paperback ISBN: 9780443288548
- eBook ISBN: 9780443288555
GR
Gerasimos Rigatos
Dr. Rigatos is Research Director at the Industrial Systems Institute, Greece. He has led research cooperation projects in the areas of nonlinear control, nonlinear filtering, and control of distributed parameter systems. His results have appeared in well-received research monographs. Since 2007, he has been awarded visiting professor positions at several academic institutes (Université Paris XI, France; Harper-Adams University College, UK; University of Northumbria, UK; University of Salerno, Italy; École Centrale de Nantes, France). He is a Senior Member of IEEE and a Member and CEng of the IET. He holds an Editor's position for the International Journal of Advanced Robotic Systems (SAGE) and for the Journal of Electrified Vehicles (SAE International). He received a PhD from the Department of Electrical and Computer Engineering of the National Technical University of Athens (NTUA), Greece, in 2000.
Affiliations and expertise
Research Director, Unit of Industrial Automation, Industrial Systems Institute, Rion Patras, GreecePS
Pierluigi Siano
Dr. Siano received a PhD in Information and Electrical Engineering from the University of Salerno, Salerno, Italy, in 2006. He is a Professor and the Scientific Director of the Smart Grids and Smart Cities Laboratory with the Department of Management and Innovation Systems of the University of Salerno. Since 2021, he has been a Distinguished Visiting Professor in the Department of Electrical and Electronic Engineering Science at the University of Johannesburg, South Africa. His research activities are focused on demand response, energy management, the integration of distributed energy resources in smart grids, electricity markets, and planning and management of power systems. He is a prolific author and, in 2019, 2020, and 2021, was awarded as a Highly Cited Researcher in Engineering by the ISI Web of Science Group. He is Editor for several IEEE journals, including 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.
Affiliations and expertise
Professor, Department of Management and Innovation Systems, University of Salerno, Fisciano, ItalyMA
Masoud Abbaszadeh
Masoud Abbaszadeh received a PhD in Electrical and Computer Engineering from the University of Alberta in 2008. From 2008 to 2011, he was with Maplesoft, Ontario, Canada, as a Research Engineer. He was the principal developer of the MapleSim Control Design Toolbox and a member of a Maplesoft-Toyota joint research team. 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 modeling and simulation. Since 2013, he has been with GE Research Center, NY, USA, where he is currently a Principal Research Engineer and technical leader of the cyber-physical security and resilience portfolio. His research interests include estimation and detection theory, robust and nonlinear control, and machine learning with applications in cyber-physical resilience. He is an Associate Editor of IEEE Transactions on Control Systems Technology, a member of the IEEE CSS Conference Editorial Board, and has over 60 patents credited to his name.
Affiliations and expertise
Principal Research Engineer, GE Global Research, Niskayuna, NY, USAPW
Patrice Wira
Dr. Wira received a PhD in Electrical Engineering from Université de Haute Alsace in 2002. He has published several journal papers, book chapters, and conference papers. He has also chaired and co-chaired numerous sessions and special sessions on intelligent control and machine learning issues. His current research interests focus on artificial neural networks, adaptive control systems, neuro-control, and especially their applicability to robotics and active power filtering. He has been a senior member of IEEE since 2013. He has served as an associate editor for the Energy section of the Heliyon Journal (Cell Press) since 2022.
Affiliations and expertise
Professor, Institut de Recherche en Informatique, Mathématiques, Automatique et Signal, Université de Haute Alsace; Director, Institut Universitaire de Technologie de Mulhouse, Mulhouse, France