Multi-Objective Trajectory Optimization for a Scaled Vehicle

Abstract

The introduction of control systems in the automotive industry has significantly increased safety. Improved control over the vehicle dynamics has been shown to contribute to substantial reductions in the number of deaths and serious injuries resulting from road traffic crashes. The introduction of Electronic Stability Control yielded impressive improvement in vehicle stability. A meta-analysis revealed that a 49% reduction of single-vehicle accidents is realized. Recent research continues the development of a fully autonomously operating vehicle. The vehicle requires the ability to operate in all situations safely, in order to reach the highest level of automation. Vehicle performance should be guaranteed within, at and beyond the limits of friction. Previous research revealed that the unstable drift motion could enlarge the operating envelope of a vehicle. An extensive amount of research is dedicated to controlling a drift. The results show that control systems are increasingly capable of stabilizing a steady-state cornering scenario. The main limitation of these studies is that only a portion of the vehicle motion that is observed in reality can be considered to be steady-state motion.

This thesis presents a multi-objective trajectory optimization which extends the steady-state analysis to a dynamic driving scenario. Based on experimental data obtained with a 1:10 scaled vehicle, accurate vehicle and tire models are derived. It is validated that the models closely mimic the dynamics of the scaled vehicle. In order to justify the use of drifting, the differences between stable and unstable driving equilibria are studied. The stability and controllability are assessed through the construction of the phase portraits and the computation of the Controllability Grammian. The findings, obtained under the assumption of steady-state conditions, are then validated in the dynamic driving scenario. A two-step optimization approach is presented. Spline optimization based on a simplified model is used to obtain initial conditions for a high fidelity model-based optimization. The scope is limited to a single corner, which is optimized under varying velocities and friction conditions.

Under the assumption of steady-state conditions, it is found that the drift motion imposes various benefits over normal driving. Higher cornering velocities and therewith yaw rates can be achieved in a drift. Besides, the principles of tire saturation and force coupling allow for controlling the lateral and yaw dynamics of the vehicle through the rear longitudinal tire force. This increases the maneuverability of the vehicle. The results of the dynamic optimization extend the findings of the steady-state analysis. In the dynamic maneuvers, drifting is found to improve vehicle maneuverability at high velocities and in scenarios of low friction. The approach presented in this work forms a basis for studying the effects that drifting could have on vehicle motion in reality. The relevant aspects of vehicle motion are translated into a multi-objective optimization. The methods that are developed in this work release the simplifying assumption of steady-state driving conditions. As a result, the drift motion can be studied in a more realistic driving scenario. It is expected that through further improving the optimization algorithm, the full operation envelope of the vehicle can be explored.