In this paper, we propose a real-time nonlinear model predictive control (NMPC) strategy for stabilization of a vehicle near the limit of lateral acceleration using the rear axle electric torque vectoring configuration of an electric vehicle. A nonlinear four-wheel vehicle model that neglects the wheel dynamics is coupled with a nonlinear tire model to design three MPC strategies of different levels of complexity that are implementable online: one that uses a linearized version of the vehicle model and then solves the resulting quadratic program problem to compute the necessary longitudinal slips on the rear wheels, a second one that employs the real-time iteration scheme on the NMPC problem, and a third one that applies the primal dual interior point method on the NMPC problem instead until convergence. Then, a sliding mode slip controller is used to compute the necessary torques on the rear wheels based on the requested longitudinal slips. After analyzing the relative tradeoffs in performance and computational cost between the three MPC strategies by comparing them against the optimal solution in a series of simulation studies, we test the most promising solution in a high-fidelity environment.

This paper presents the development of path-tracking control strategies for an over-actuated autonomous electric vehicle. The vehicle platform is equipped with four-wheel steering (4WS) as well as torque vectoring (TV) capabilities, which enable the control of vehicle dynamics to be enhanced. A nonlinear model predictive controller is proposed taking into account the nonlinearities in vehicle dynamics at the limits of handling as well as the crucial actuator constraints. Controllers with different actuation formulations are presented and compared to study the path-tracking performance of the vehicle with different levels of actuation. The controllers are implemented in a high-fidelity simulation environment considering scenarios of vehicle handling limits. According to the simulation results, the vehicle achieves the best overall path-tracking performance with combined 4WS and TV, which illustrates that the over-actuation topology can enhance the path-tracking performance during conditions under the limits of handling. In addition, the performance of the over-actuation controller is further assessed with different sampling times as well as prediction horizons in order to investigate the effect of such parameters on the control performance, and its capability for real-time execution. In the end, the over-actuation control strategy is implemented on a target machine for real-time validation. The control formulation proposed in this paper is proven to be compatible with different levels of actuation, and it is also demonstrated in this work that it is possible to include the particular over-actuation formulation and specific nonlinear vehicle dynamics in real-time operation, with the sampling time and prediction time providing a compromise between path-tracking performance and computational time.

Automated vehicles are expected to push towards the evolution of the mobility environment in the near future by increasing vehicle stability and decreasing commute time and vehicle fuel consumption. One of the main limitations they face is motion sickness (MS), which can put their wide impact at risk, as well as their acceptance by the public. In this direction, this paper presents the application of motion planning in order to minimise motion sickness in automated vehicles. Thus, an optimal control problem is formulated through which we seek the optimum velocity profile for a predefined road path for multiple fixed journey time (JT) solutions. In this way, a Pareto Front will be generated for the conflicting objectives of MS and JT. Despite the importance of optimising both of these, the optimum velocity profile should be selected after taking into consideration additional objectives. Therefore, as the optimal control is focused on the MS minimisation, a sorting algorithm is applied to seek the optimum solution among the pareto alternatives of the fixed time solutions. The aim is that this solution will correspond to the best velocity profile that also ensures the optimum compromise between motion comfort, safety and driving behaviour, energy efficiency, journey time and riding confidence.

This paper presents a comprehensive framework for optimising vehicle performance, integrating advanced simulation techniques with optimisation methodologies. The aim is to find the best racing line, as well as the optimal combination of parameters and control inputs to make a car as fast as possible around a given track, with a focus on energy deployment and recovery, active torque distribution and active aerodynamics. The problem known as the Minimum Lap Time Problem is solved using optimal control methods and direct collocation. The solution covers the modelling of the track, vehicle dynamics, active aerodynamics, and a comprehensive representation of the powertrain including motor, engine, transmission, and drivetrain components. This integrated simulator allows for the exploration of different vehicle configurations and track layouts, providing insights into optimising vehicle design and vehicle control simultaneously for improved performance and energy efficiency. Test results demonstrate the effect of active torque distribution on performance under various conditions, enhanced energy efficiency and performance through regenerative braking, and the added value of including parameter optimisation within the optimisation framework. Notably, the simulations revealed interesting behaviours similar to lift-and-coast strategies, depending on the importance of energy saving, thereby highlighting the effectiveness of the proposed control strategies. Also, results demonstrate the positive effect of active torque distribution on performance under various conditions, attributed to the higher utilization of available adherence. Furthermore, unlike a simpler single-track model, the optimal solution required fine control of the active aerodynamic systems, reflecting the complex interactions between different subsystems that the simulation can capture. Finally, the inclusion of parameter optimisation while considering all active systems, further improves performance and provides valuable insights into the impact of design choices.