Industrial-scale autonomous wheeled-vehicle path following by combining iterative learning control with feedback linearization

This paper presents a path following method for autonomous wheeled vehicles that combines iterative learning control (ILC) with nonlinear feedback linearization (FBL) to provide anticipatory control action based on stored path following errors over repeated driving trials. By implementing ILC in a fully feedback-linearized space, control corrections are applied to a transformed input, thus allowing for a single back-computation to the nonlinear vehicle's control input. Hence, the approach is comparatively easy to implement and also computationally inexpensive. We first outline the mathematical formulation for this control method and then describe field results from tests conducted by using an industrial-scale wheeled underground mining vehicle in a representative environment to demonstrate effectiveness.