In complete control; simultaneous path, speed and sideslip angle control of a drifting automobile

Professional drivers can control their vehicle's position, speed and orientation with an incredible amount of precision even when the open loop dynamics are unstable. They do so by leveraging their understanding of the vehicle's behavior and simultaneous coordination of the vehicle's inputs. As autonomous vehicles are developed, they should be able to drive just as well as professional drivers to ensure successful navigation of emergency scenarios involving unintended rear tire saturation on the road. With that goal, this thesis presents improvements to the existing state of the art of autonomous vehicle drift control supported by experiments using a heavily modified 1981 DMC DeLorean, MARTY. We first present a novel technique to achieve a fully actuated system while drifting. Drawing inspiration from professional drivers, we use brakes along with steering and drive torque simultaneously and convert the system into a fully actuated one. Equilibrium analysis leveraging this simultaneous actuation confirms the expansion of the existing drift equilibria for a given curvature from a curve on the speed-sideslip plane to an area. Similarly, a tangent space analysis confirms that front braking adds another dimension to the available state derivatives; from a surface in the under-actuated case, to a volume. This represents a significant increase in the set of trajectories available to a drifting vehicle, and also an avenue to reliably reduce energy from the system while maintaining control of the vehicle's sideslip and position. Leveraging this fully actuated system, we then present an architecture capable of following a path, while tracking a desired sideslip and speed. The formulation builds upon the existing state of the are of drift control by incorporating speed tracking and precisely coordinating the three inputs. Experimental results using MARTY verify independent control over the position, speed and sideslip through a variety of trajectories while operating within the limits of actuation. They also highlight some limitations when operating at the limits of actuation. These limitations lead to the development of a novel application of nonlinear model predictive control for drifting. The model predictive control framework directly addresses the challenges of arbitrary error dynamics and actuator saturation as the vehicle model and actuation limits are easily embedded in the framework itself. The prediction horizon is capable of trading off between the different objectives when required similar to a professional driver. Experimental results illustrate significantly improved path tracking performance over the existing state of the art. They further demonstrate successful selective prioritization of different objectives. The controller can navigate difficult dynamic trajectories at the limits of actuation where the previous controller exhibited degraded performance. Finally, an augmented nonlinear model predictive control framework expands the operational domain of the vehicle to include the ability to track drift transitions. These highly dynamic maneuvers switch the direction of travel from left to right or vice versa and require operation in highly transient regions of the state space. Successful tracking of `Figure 8' trajectories verifies the expanded operational domain. Comparisons with the original formulation highlight the importance of the augmentations. Designing Figure 8 trajectories which leverage additional braking actuation has the potential to improve performance, however, requires additional model fidelity to be implemented. These contributions significantly increase the operational domain of a drifting vehicle, improve tracking performance, and mitigate the adverse effects of actuator saturation. They extend the applicability of drift controllers to common vehicles on the road which have much more limited actuation than purpose built drift cars. We hope that these contributions will form the basis of advanced safety systems and help improve vehicle safety

Keywords: Mechanical Engineering.
Advisor: Gerdes J. Christian
Pavone Marco
Rock Stephen M.