Lateral and Longitudinal Coordinated Control of Intelligent Vehicle Based on High-Precision Dynamics Model under High-Speed Limit Condition

This study focuses on improving the trajectorytracking control for intelligent vehicles in high-speed and largecurvature limit conditions. To this end, a high-precision fivedegree-of-freedom (5-DOF) dynamics model (HPM) that incorporates suspension characteristics is introduced. Furthermore, acoordinated lateral and longitudinal control system is developed.The lateral model predictive control (MPC) involves two crucialstages: initially, a desired trajectory with associated speed datais generated based on path curvature. Subsequently, using thehigh-precision 5-DOF dynamics model, an objective functionis formulated to minimize the difference between the vehicle’scurrent state and the desired state. This process determines theoptimal front wheel steering angle, taking into account vehiclepositional constraints and steering limitations. Additionally, adouble proportional–integral–derivative (PID) controller for longitudinal control adjusts the throttle and brake pressure basedon real-time position and speed data, ensuring integrated controlover both lateral and longitudinal movements. The effectivenessof this approach is confirmed through real vehicle testing andsimulation. Results show that the high-precision 5-DOF dynamicsmodel markedly enhances the accuracy of vehicle response modeling, and the coordinated control system successfully executesprecise trajectory tracking. In extreme scenarios of high-speedand large curvature, the enhanced model substantially improvestrajectory accuracy and driving stability, thus promoting safevehicle operation

Lateral and Longitudinal Coordinated Control of Intelligent Vehicle Based on High-Precision Dynamics Model under High-Speed Limit Condition

This study focuses on improving the trajectorytracking control for intelligent vehicles in high-speed and largecurvature limit conditions. To this end, a high-precision fivedegree-of-freedom (5-DOF) dynamics model (HPM) that incorporates suspension characteristics is introduced. Furthermore, acoordinated lateral and longitudinal control system is developed.The lateral model predictive control (MPC) involves two crucialstages: initially, a desired trajectory with associated speed datais generated based on path curvature. Subsequently, using thehigh-precision 5-DOF dynamics model, an objective functionis formulated to minimize the difference between the vehicle’scurrent state and the desired state. This process determines theoptimal front wheel steering angle, taking into account vehiclepositional constraints and steering limitations. Additionally, adouble proportional–integral–derivative (PID) controller for longitudinal control adjusts the throttle and brake pressure basedon real-time position and speed data, ensuring integrated controlover both lateral and longitudinal movements. The effectivenessof this approach is confirmed through real vehicle testing andsimulation. Results show that the high-precision 5-DOF dynamicsmodel markedly enhances the accuracy of vehicle response modeling, and the coordinated control system successfully executesprecise trajectory tracking. In extreme scenarios of high-speedand large curvature, the enhanced model substantially improvestrajectory accuracy and driving stability, thus promoting safevehicle operation

Torque Vectoring Predictive Control of a Four In-Wheel Motor Drive Electric Vehicle

The recent integration of vehicles with electrified powertrains in the automotive sector provides higher energy efficiency, lower pollution levels and increased controllability. These features have led to an increasing interest in the development of Advanced Driver- Assistance Systems (ADAS) that enhance not only the vehicle dynamic behaviour, but also its efficiency and energy consumption. This master’s thesis presents some contributions to the vehicle modeling, parameter estimation, model predictive control and reference generation applied to electric vehicles, paying particular attention to both model and controller validation, leveraging offline simulations and a real-time driving simulator. The objective of this project is focused on the Nonlinear Model Predictive Controller (NMPC) technique developing torque distribution strategies, specifically Torque Vectoring (TV) for a four-in wheel motor drive electric vehicle. A real-time TV-NMPC algorithm will be implemented, which maximizes the wheels torque usage and distribution to enhance vehicle stability and improve handling capabilities. In order to develop this control system, throughout this thesis the whole process carried out including the implementation requirements and considerations are described in detail. As the NMPC is a model-based approach, a nonlinear vehicle model is proposed. The vehicle model, the estimated parameters and the controller will be validated through the design of open and closed loop driving maneuvers for offline simulations performed in a simulation plant (VI-CarRealTime) and by means of a real-time driving simulator (VI-Grade Compact Simulator) to test the vehicle performance through various dynamic driving conditions

Torque Vectoring Predictive Control of a Four In-Wheel Motor Drive Electric Vehicle

The recent integration of vehicles with electrified powertrains in the automotive sector provides higher energy efficiency, lower pollution levels and increased controllability. These features have led to an increasing interest in the development of Advanced Driver- Assistance Systems (ADAS) that enhance not only the vehicle dynamic behaviour, but also its efficiency and energy consumption. This master’s thesis presents some contributions to the vehicle modeling, parameter estimation, model predictive control and reference generation applied to electric vehicles, paying particular attention to both model and controller validation, leveraging offline simulations and a real-time driving simulator. The objective of this project is focused on the Nonlinear Model Predictive Controller (NMPC) technique developing torque distribution strategies, specifically Torque Vectoring (TV) for a four-in wheel motor drive electric vehicle. A real-time TV-NMPC algorithm will be implemented, which maximizes the wheels torque usage and distribution to enhance vehicle stability and improve handling capabilities. In order to develop this control system, throughout this thesis the whole process carried out including the implementation requirements and considerations are described in detail. As the NMPC is a model-based approach, a nonlinear vehicle model is proposed. The vehicle model, the estimated parameters and the controller will be validated through the design of open and closed loop driving maneuvers for offline simulations performed in a simulation plant (VI-CarRealTime) and by means of a real-time driving simulator (VI-Grade Compact Simulator) to test the vehicle performance through various dynamic driving conditions

A Human Driver Model for Autonomous Lane Changing in Highways: Predictive Fuzzy Markov Game Driving Strategy

This study presents an integrated hybrid solution to mandatory lane changing problem to deal with accident avoidance by choosing a safe gap in highway driving. To manage this, a comprehensive treatment to a lane change active safety design is proposed from dynamics, control, and decision making aspects. My effort first goes on driver behaviors and relating human reasoning of threat in driving for modeling a decision making strategy. It consists of two main parts; threat assessment in traffic participants, (TV s) states, and decision making. The first part utilizes an complementary threat assessment of TV s, relative to the subject vehicle, SV , by evaluating the traffic quantities. Then I propose a decision strategy, which is based on Markov decision processes (MDPs) that abstract the traffic environment with a set of actions, transition probabilities, and corresponding utility rewards. Further, the interactions of the TV s are employed to set up a real traffic condition by using game theoretic approach. The question to be addressed here is that how an autonomous vehicle optimally interacts with the surrounding vehicles for a gap selection so that more effective performance of the overall traffic flow can be captured. Finding a safe gap is performed via maximizing an objective function among several candidates. A future prediction engine thus is embedded in the design, which simulates and seeks for a solution such that the objective function is maximized at each time step over a horizon. The combined system therefore forms a predictive fuzzy Markov game (FMG) since it is to perform a predictive interactive driving strategy to avoid accidents for a given traffic environment. I show the effect of interactions in decision making process by proposing both cooperative and non-cooperative Markov game strategies for enhanced traffic safety and mobility. This level is called the higher level controller. I further focus on generating a driver controller to complement the automated car’s safe driving. To compute this, model predictive controller (MPC) is utilized. The success of the combined decision process and trajectory generation is evaluated with a set of different traffic scenarios in dSPACE virtual driving environment. Next, I consider designing an active front steering (AFS) and direct yaw moment control (DYC) as the lower level controller that performs a lane change task with enhanced handling performance in the presence of varying front and rear cornering stiffnesses. I propose a new control scheme that integrates active front steering and the direct yaw moment control to enhance the vehicle handling and stability. I obtain the nonlinear tire forces with Pacejka model, and convert the nonlinear tire stiffnesses to parameter space to design a linear parameter varying controller (LPV) for combined AFS and DYC to perform a commanded lane change task. Further, the nonlinear vehicle lateral dynamics is modeled with Takagi-Sugeno (T-S) framework. A state-feedback fuzzy H∞ controller is designed for both stability and tracking reference. Simulation study confirms that the performance of the proposed methods is quite satisfactory

Transportation Mission-Based Optimization of Heavy Combination Road Vehicles and Distributed Propulsion, Including Predictive Energy and Motion Control

This thesis proposes methodologies to improve heavy vehicle design by reducing the total cost of ownership and by increasing energy efficiency and safety.Environmental issues, consumers expectations and the growing demand for freight transport have created a competitive environment in providing better transportation solutions. In this thesis, it is proposed that freight vehicles can be designed in a more cost- and energy-efficient manner if they are customized for narrow ranges of operational domains and transportation use-cases. For this purpose, optimization-based methods were applied to minimize the total cost of ownership and to deliver customized vehicles with tailored propulsion components that best fit the given transportation missions and operational environment. Optimization-based design of the vehicle components was found to be effective due to the simultaneous consideration of the optimization of the transportation mission infrastructure, including charging stations, loading-unloading, routing and fleet composition and size, especially in case of electrified propulsion. Implementing integrated vehicle hardware-transportation optimization could reduce the total cost of ownership by up to 35% in the case of battery electric heavy vehicles. Furthermore, in this thesis, the impacts of two future technological advancements, i.e., heavy vehicle electrification and automation, on road freight transport were discussed. It was shown that automation helps the adoption of battery electric heavy vehicles in freight transport. Moreover, the optimizations and simulations produced a large quantity of data that can help users to select the best vehicle in terms of the size, propulsion system, and driving system for a given transportation assignment. The results of the optimizations revealed that battery electric and hybrid heavy combination vehicles exhibit the lowest total cost of ownership in certain transportation scenarios. In these vehicles, propulsion can be distributed over different axles of different units, thus the front units may be pushed by the rear units. Therefore, online optimal energy management strategies were proposed in this thesis to optimally control the vehicle motion and propulsion in terms of the minimum energy usage and lateral stability. These involved detailed multitrailer vehicle modeling and the design and solution of nonlinear optimal control problems

Narrow Urban Vehicles with an Integrated Suspension Tilting System: Design, Modeling, and Control

Narrow urban vehicles are proposed to alleviate urban transportation challenges like congestion, parking, fuel consumption, and pollution. They are designed to seat one or two people in tandem, which saves space in road infrastructures as well as improves the fuel efficiency. However, to overcome the high rollover tendency which comes as a consequence of reduced track-width ratio, tilting systems for vehicle roll motion control are suggested. Existing tilting solutions, which mechanically connect the wheel modules on both sides for motion synchronization, are not space-friendly for the narrow vehicle footprint. The mechanical linkages also add extra weight to those urban vehicles initially designed to be light-weighted. A novel integrated suspension tilting system (ISTS) is proposed in this thesis, which replaces rigid mechanical linkages with flexible hydraulic pipes and cylinders. In addition, combining the suspension and tilting into an integrated system will result in even more compact, light-weighted, and spacious urban vehicles. The concept is examined, and the suspension mechanism for the tilting application is proposed after examining various mechanisms for their complexity and space requirements. Kinematic and dynamic properties of the tilting vehicle under large suspension strokes are analyzed to optimize the mechanism design. Control of the active tilting systems for vehicle roll stability improvement is then discussed. Rather than tilting the vehicle to entirely eliminate the lateral load transfer during cornering, an integrated envelope approach considering both lateral and roll motion is proposed to improve the energy efficiency while maintaining the vehicle stability. A re-configurable integrated control structure is also developed for various vehicle configurations as well as enhancing the system robustness against actuator failures. The model predictive control (MPC) scheme is adopted considering the non-minimum phase nature of active tilting systems. The predictive feature along with the proposed roll envelope formulation provides a framework to balance the transient and steady-state performances using the tilting actuators. The suggested controller is firstly demonstrated on a vehicle roll model, and then applied to high-fidelity full vehicle models in CarSim including a four-wheeled SUV as well as a three-wheeled narrow urban vehicle. The SUV simulation results indicate the potential of using the developed envelope controller on conventional vehicles with active suspensions, while the narrow urban vehicle simulations demonstrate the feasibility of using the suggested ISTS on narrow tilting vehicles. By adopting the integrated envelope control approach, actuation effort is reduced and the vehicle handling, along with the stability in both lateral and roll, can be further improved

Holistic Vehicle Control Using Learning MPC

In recent years, learning MPC schemes have been introduced to address these challenges of traditional MPC. They typically leverage different machine learning techniques to learn the system dynamics directly from data, allowing it to handle model uncertainty more effectively. Besides, they can adapt to changes by continuously updating the learned model using real-time data, ensuring that the controller remains effective even as the system evolves. However, there are some challenges for the existing learning MPC techniques. Firstly, learning-based control approaches often lack interpretability. Understanding and interpreting the learned models and their learning and prediction processes are crucial for safety critical systems such as vehicle stability systems. Secondly, existing learning MPC techniques rely solely on learned models, which might result in poor performance or instability if the model encounters scenarios that differ significantly from the training data. Thirdly, existing learning MPC techniques typically require large amounts of high-quality data for training accurate models, which can be expensive or impractical in the vehicle stability control domain. To address these challenges, this thesis proposes a novel hybrid learning MPC approach for HVC. The main objective is to leverage the capabilities of machine learning algorithms to learn accurate and adaptive models of vehicle dynamics from data, enabling enhanced control strategies for improved stability and maneuverability. The hybrid learning MPC scheme maintains a traditional physics-based vehicle model and a data-based learning model. In the learned model, a variety of machine-learning techniques can be used to predict vehicle dynamics based on learning from collected vehicle data. The performance of the developed hybrid learning MPC controller using torque vectoring (TV) as the actuator is evaluated through the Matlab/Simulink and CarSim co-simulation with a high-fidelity Chevy Equinox vehicle model under a series of harsh maneuvers. Extensive real-world experiments using a Chevy Equinox electric testing vehicle are conducted. Both simulation results and experimental results show that the developed hybrid learning MPC approach consistently outperforms existing MPC methods with better yaw rate tracking performance and smaller vehicle sideslip under various driving conditions

Advanced Control and Estimation Concepts, and New Hardware Topologies for Future Mobility

According to the National Research Council, the use of embedded systems throughout society could well overtake previous milestones in the information revolution. Mechatronics is the synergistic combination of electronic, mechanical engineering, controls, software and systems engineering in the design of processes and products. Mechatronic systems put “intelligence” into physical systems. Embedded sensors/actuators/processors are integral parts of mechatronic systems. The implementation of mechatronic systems is consistently on the rise. However, manufacturers are working hard to reduce the implementation cost of these systems while trying avoid compromising product quality. One way of addressing these conflicting objectives is through new automatic control methods, virtual sensing/estimation, and new innovative hardware topologies