Autonomous Electric Vehicles
Nonlinear Control, Traction, and Propulsion
- 1st Edition - March 19, 2025
- Imprint: Elsevier
- Authors: Gerasimos Rigatos, Masoud Abbaszadeh, Pierluigi Siano, 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
Autonomous Electric Vehicles explores cutting-edge technologies revolutionizing transportation and city navigation. Novel solutions to the control problem of the complex nonli… Read more
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Request a sales quoteAutonomous 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.
- 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
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. R&D engineering professionals engaged in electric powertrain design and development, autonomous vehicle dynamics and control, and intelligent transport systems for the net-zero transition.
- Title of Book
- Cover image
- Title page
- Table of Contents
- Copyright
- Dedication
- Overview
- Preface
- Glossary
- Part One: Control and estimation of robotized vehicles' dynamics and kinematics
- 1: Nonlinear control of ground vehicles I
- 1.1. Nonlinear optimal control for cooperation of car-like front-wheel-steered ground vehicles
- 1.1.1. Outline
- 1.1.2. Kinematic model of the front-wheel steered car-like vehicle
- 1.1.3. Approximate linearization of the vehicle's kinematic model
- 1.1.4. The nonlinear H∞ control
- 1.1.5. Lyapunov stability analysis
- 1.1.6. Simulation tests
- 1.1.7. Synchronization between a leader and multi-follower vehicles
- 1.2. Nonlinear optimal control of an omnidirectional three-wheel mobile robot
- 1.2.1. Outline
- 1.2.2. Dynamic model of the 3-wheel omnidirectional mobile robot
- 1.2.3. Design of a nonlinear optimal controller
- 1.2.4. Design of a multi-loop flatness-based controller for the 3-wheel robot
- 1.2.5. Simulation tests
- 2: Nonlinear control of ground vehicles II
- 2.1. Nonlinear optimal control of four-wheel autonomous ground vehicles
- 2.1.1. Outline
- 2.1.2. Dynamic and kinematic model of the vehicle
- 2.1.3. Approximate linearization of the four-wheel vehicle dynamics
- 2.1.4. The nonlinear H∞ control
- 2.1.5. Lyapunov stability analysis
- 2.1.6. Robust state estimation with the use of the H∞ Kalman Filter
- 2.1.7. Simulation tests
- 2.2. Nonlinear optimal control of tracked vehicles
- 2.2.1. Outline
- 2.2.2. Kinematic model of the tracked mobile robot
- 2.2.3. Approximate linearization of the model of the tracked vehicle
- 2.2.4. Lyapunov stability analysis
- 2.2.5. State estimation with robust Kalman Filtering
- 2.2.6. Simulation tests
- 2.3. Nonlinear optimal control for multi-axle multi-steered autonomous vehicles
- 2.3.1. Outline
- 2.3.2. Kinematic model of the multi-axle multi-steered vehicle
- 2.3.3. Differential flatness of the five-axle and three-steering vehicle system
- 2.3.4. Linearization of the model of the five-axle and three-steering vehicle
- 2.3.5. A nonlinear optimal controller for the five-axle and three-steering vehicle
- 2.3.6. Lyapunov stability analysis
- 2.3.7. Simulation tests
- 3: Nonlinear control of aerial vehicles I
- 3.1. Nonlinear optimal control for UAVs with tilting rotors
- 3.1.1. Outline
- 3.1.2. Dynamic model of the tilt-rotor UAV
- 3.1.3. Linearization and design of a stabilizing H∞ feedback controller
- 3.1.4. Lyapunov stability analysis
- 3.1.5. Simulation tests
- 3.2. Nonlinear optimal control of a 6-DOF fixed-wing UAV
- 3.2.1. Outline
- 3.2.2. Dynamic model of the 6-DOF fixed-wing unmanned aerial vehicle
- 3.2.3. Differential flatness properties of the UAV
- 3.2.4. Approximate linearization of the dynamic model of the fixed-wing UAV
- 3.2.5. Design of a nonlinear H∞ controller for the fixed-wing UAV
- 3.2.6. Lyapunov stability analysis
- 3.2.7. Simulation tests
- 3.3. Flatness-based control in successive loops for octocopters
- 3.3.1. Outline
- 3.3.2. Dynamic model of the octocopter UAV
- 3.3.3. Flatness-based control in successive loops for the octocopter UAV
- 3.3.4. Simulation tests
- 4: Nonlinear control of aerial vehicles II
- 4.1. Flatness-based control in successive loops for autonomous quadrotors
- 4.1.1. Outline
- 4.1.2. Dynamic model of 6-DOF quadrotors
- 4.1.3. Flatness-based control in successive loops for 6-DOF quadropters
- 4.1.4. Simulation tests
- 4.2. Nonlinear optimal and multi-loop flatness-based control for dual-UAV cooperative payload transportation
- 4.2.1. Outline
- 4.2.2. Dynamic model of the cable-driven payload
- 4.2.3. Dynamic model of 6-DOF autonomous quadrotors
- 4.2.4. Nonlinear optimal control for the cable-suspended payload
- 4.2.5. The nonlinear H∞ control
- 4.2.6. Flatness-based control in successive loops for the quadrotors
- 4.2.7. Simulation tests
- 5: Nonlinear control of unmanned vessels
- 5.1. Flatness-based control in successive loops for 3-DOF autonomous underwater vessels
- 5.1.1. Outline
- 5.1.2. Flatness-based control in successive loops for 3-DOF autonomous underwater vessels
- 5.1.3. Simulation tests
- 5.2. Flatness-based control in successive loops for the 3-DOF USV and the 6-DOF AUV
- 5.2.1. Outline
- 5.2.2. Dynamic model of 3-DOF unmanned surface vessels
- 5.2.3. Flatness-based control in successive loops for 3-DOF unmanned surface vessels
- 5.2.4. Kinematic and dynamic model of 6-DOF autonomous underwater vessels
- 5.2.5. Flatness-based control in successive loops for 6-DOF autonomous underwater vessels
- 5.2.6. Simulation tests
- Part Two: Control and estimation of the electric traction system for such autonomous vehicles
- 6: Nonlinear control of electric traction systems based on three-phase motors
- 6.1. Flatness-based control in successive loops for VSI-fed PMSMs and induction motors
- 6.1.1. Outline
- 6.1.2. Differential flatness properties of the VSI-PMSM
- 6.1.3. Flatness-based control in successive loops of the VSI-PMSM
- 6.1.4. Differential flatness properties of the VSI-fed IM
- 6.1.5. Flatness-based control in successive loops of the VSI-IM
- 6.1.6. Simulation tests
- 6.2. Nonlinear optimal and sliding-mode control for Voltage Source Inverter-fed Induction Motors
- 6.2.1. Outline
- 6.2.2. Dynamic model of the induction motor
- 6.2.3. Dynamic model of the three-phase voltage source inverter
- 6.2.4. Dynamic model of the VSI-fed induction motor
- 6.2.5. Proof of differential flatness properties of the VSI-fed induction motor
- 6.2.6. Linearization and control of the VSI-fed induction motor
- 6.2.7. Stabilizing feedback control
- 6.2.8. Lyapunov stability analysis
- 6.2.9. Comparison to other control methods for the VSI-fed IM
- 6.2.10. Simulation tests
- 6.3. Nonlinear optimal control for a flywheel and battery-based powertrain of Electric Vehicles
- 6.3.1. Outline
- 6.3.2. Dynamic model of the flywheel-based powertrain of the EV
- 6.3.3. Integrated dynamic model of the EV powertrain
- 6.3.4. Differential flatness properties of the flywheel-based powertrain
- 6.3.5. Approximate linearization of the dynamics of the EV powertrain
- 6.3.6. Design of an H∞ feedback controller
- 6.3.7. Lyapunov stability analysis
- 6.3.8. Simulation tests
- 7: Nonlinear control of electric traction systems based on multi-phase motors
- 7.1. Nonlinear optimal control for the 5-phase induction motor-based traction of electric vehicles
- 7.1.1. Outline
- 7.1.2. Dynamic model of the 5-phase induction motor
- 7.1.3. Differential flatness properties of the 5-phase induction motor
- 7.1.4. Approximate linearization of the dynamics of the 5-phase induction motor
- 7.1.5. The nonlinear H∞ control
- 7.1.6. Lyapunov stability analysis
- 7.1.7. Simulation tests
- 7.2. Nonlinear optimal control for VSI-fed 6-phase PMSMs in the traction of electric vehicles
- 7.2.1. Outline
- 7.2.2. Dynamic model of the VSI-fed 6-phase PMSM
- 7.2.3. State-space model of the inverters
- 7.2.4. Differential flatness properties of the model of the VSI-fed 6-phase PMSM
- 7.2.5. Approximate linearization of the model of the VSI-fed 6-phase PMSM
- 7.2.6. Lyapunov stability analysis
- 7.2.7. Simulation tests
- 7.3. Nonlinear optimal control for the 9-phase permanent magnet synchronous motor
- 7.3.1. Outline
- 7.3.2. Dynamic model of the 9-phase PMSM
- 7.3.3. Differential flatness properties of the 9-phase PMSM
- 7.3.4. Approximate linearization of the dynamic model of the 9-phase PMSM
- 7.3.5. Design of a stabilizing H∞ feedback controller
- 7.3.6. Lyapunov stability analysis
- 7.3.7. Simulation tests
- 8: Nonlinear control of EV auxiliary actuation systems
- 8.1. Nonlinear control of electrohydraulic actuators
- 8.1.1. Outline
- 8.1.2. Flatness-based control in successive loops for electrohydraulic actuators
- 8.1.3. Simulation tests
- 8.2. Flatness-based control in successive loops of electropneumatic actuators
- 8.2.1. Outline
- 8.2.2. Dynamic model of the electropneumatic actuator
- 8.2.3. Flatness-based control in successive loops for the electropneumatic actuator
- 8.2.4. Simulation tests
- 8.3. Flatness-based control in successive loops of a PMLSM-driven clutch
- 8.3.1. Outline
- 8.3.2. Dynamic model of the PMLSM-actuated clutch
- 8.3.3. Flatness-based control in successive loops for the PMLSM-actuated clutch
- 8.3.4. Simulation tests
- A: Nonlinear optimal control and Lie algebra-based control
- A.1. Control based on approximate linearization
- A.1.1. Overview of the optimal control concept
- A.1.2. Design of an H∞ nonlinear optimal controller
- A.1.3. Optimal state estimation with the H∞ Kalman Filter
- A.2. Global linearization-based control concepts
- A.2.1. Foundations of global linearization-based control
- A.2.2. Elaborating on input–output linearization
- A.2.3. Input-state linearization
- A.2.4. Stages in the implementation of input-state linearization
- A.2.5. Input–output and input-state linearization for MIMO systems
- A.2.6. Dynamic extension
- B: Differential flatness theory and flatness-based control methods
- B.1. Global linearization-based control with the use of differential flatness theory
- B.1.1. Background on differential flatness theory
- B.1.2. Differential flatness for finite-dimensional systems
- B.1.3. Equivalence and differential flatness
- B.1.4. Differential flatness and trajectory planning
- B.1.5. Differential flatness, feedback control, and equivalence
- B.1.6. Flatness-based control and state feedback for systems with model uncertainties
- B.1.7. Classification of differentially flat systems
- B.2. Flatness-based control of nonlinear dynamical systems in cascading loops
- B.2.1. Decomposition of the state-space model into cascading differentially flat subsystems
- B.2.2. Tracking error dynamics for flatness-based control in successive loops
- B.2.3. Comparison to backstepping control for nonlinear systems
- Epilogue
- References
- Index
- Edition: 1
- Published: March 19, 2025
- Imprint: Elsevier
- No. of pages: 660
- Language: English
- Paperback ISBN: 9780443288548
- eBook ISBN: 9780443288555
GR
Gerasimos Rigatos
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.
Affiliations and expertise
Research Director, Unit of Industrial Automation, Industrial Systems Institute, Rion Patras, GreeceMA
Masoud Abbaszadeh
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.
Affiliations and expertise
GE Vernova Advanced Research Center, Niskayuna, NY, USAPS
Pierluigi Siano
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.
Affiliations and expertise
Professor, Department of Management and Innovation Systems, University of Salerno, Fisciano, ItalyPW
Patrice Wira
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.
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, FranceRead Autonomous Electric Vehicles on ScienceDirect