Nonlinear Control of a Ground Vehicle using Data-Driven Dynamic Models

Abstract

As autonomous vehicles continue to grow in popularity, it is imperative for engineers to gain greater understanding of vehicle modeling and controls under different situations. Most research has been conducted on on-road ground vehicles, yet off-road ground vehicles which also serve vital roles in society have not enjoyed the same attention. The dynamics for off-road vehicles are far more complex due to different terrain conditions and 3D motion. Thus, modeling for control applications is difficult. A potential solution may be the incorporation of empirical data for modeling purposes, which is inspired by recent machine learning advances, but requires less computation. This thesis presents results for empirical modeling of an off-road ground vehicle, Polaris XP 900. As a first step, data was collected for 2D planar motion by obtaining several velocity step responses. Multivariable polynomial surface fits were performed for the step responses. Sliding mode control layered on top of pure pursuit guidance is then used to drive the vehicle for waypoint following, using the empirical model. Simulation and experimental results show that the vehicle can perform waypoint following for a circular and sinusoidal with minimal error. Furthermore, more experimental data was collected to show the effects of adaptive velocity and adaptive lookahead for path tracking. A comparison of the controller's performance was also explored between on-road and off-road terrain.