  A reliable controller is critical for execution of safe and smooth maneuvers
of an autonomous vehicle. The controller must be robust to external
disturbances, such as road surface, weather, wind conditions, and so on. It
also needs to deal with internal variations of vehicle sub-systems, including
powertrain inefficiency, measurement errors, time delay, etc. These factors
introduce issues in controller performance. In this paper, a feed-forward
compensator is designed via a data-driven method to model and optimize the
controller performance. Principal Component Analysis (PCA) is applied for
extracting influential features, after which a Time Delay Neural Network is
adopted to predict control errors over a future time horizon. Based on the
predicted error, a feedforward compensator is then designed to improve control
performance. Simulation results in different scenarios show that, with the help
of with the proposed feedforward compensator, the maximum path tracking error
and the steering wheel angle oscillation are improved by 44.4% and 26.7%,
respectively.
