Model Reference Adaptive Backstepping Control of an Autonomous Ground Vehicle

Abstract

With an increased push for commercial autonomous cars, the demand of high speed systems capable of performing in unstructured driving environments is growing. In this thesis, the behavior of a bio-inspired predator prey model is considered to stimulate a more organic response to obstacles and a moving target than existing algorithms. However, the current predator prey model has a disconnect between the desired velocities commanded and the torque signals provided to the motors due the dynamics of the vehicle not accounted for. This causes the vehicle to derail from its intended trajectory at sharp turns. In this study, we start by adding dynamic behavior to the unicycle model to account for the varying dynamics of the vehicle. A backstepping algorithm is developed to connect the predator-prey model commanding desired velocities to an appropriate torque controller for the motors of the vehicle. To account for the unknown dynamic model parameters an adaptive control approach is utilized. Three different controllers are developed and evaluated. Out of the three, the indirect MRAC backstepping controller is deemed unsuitable due to its limitations with handling unknown parameter structure. The direct MRAC backstepping is deemed suitable and therefore simulated and implemented on the vehicle. The newly derived controller is able to overcome the disconnect and allow the vehicle to optimally track its trajectory for a velocity range of 1 m/s to 9 m/s despite varying dynamics. Lastly, the L1 adaptive backstepping controller is introduced and simulated to provide an alternative, more robust solution to the direct MRAC backstepping controller.