Neural Network Model Predictive Motion Control Applied to Automated Driving With Unknown Friction
Nathan A. Spielberg, Matthew Brown, J. Christian Gerdes
Abstract
Many innovative applications of vehicle control involve trajectory following while avoiding collisions, respecting actuator and dynamic limits, and using complex nonlinear dynamics. Additionally, these vehicle controllers must operate in the presence of difficult-to-model and uncertain dynamic forces which are often a function of the environment. To solve these problems, we present a design and experimental validation of neural network model predictive control (NNMPC), a method that uses vehicle operation data to construct a neural network model which is efficiently implemented in MPC. By learning a neural network model with a history of states and controls, NNMPC is capable of predicting vehicle dynamics in changing and complex operating conditions. We challenge NNMPC with the difficult task of automated racing near the friction limits without prior knowledge of the road-tire friction coefficient. The experimental results on an automated test vehicle demonstrate the capability of NNMPC to follow a trajectory near the limits on both high- and low-friction test courses. Furthermore, NNMPC outperforms a physics-based benchmark MPC on both the courses where the environmental latent state of road-tire friction is explicitly considered.