A Data Driven Method of Feedforward Compensator Optimization for Autonomous Vehicle Control

TL;DR

This paper presents a data-driven approach using PCA and neural networks to optimize a feedforward compensator, significantly enhancing autonomous vehicle control robustness and accuracy under various disturbances.

Contribution

It introduces a novel data-driven method combining PCA and Time Delay Neural Networks for designing an optimized feedforward compensator in autonomous vehicle control.

Findings

Path tracking error reduced by 44.4%

Steering wheel oscillation decreased by 26.7%

Improved control performance in diverse scenarios

Abstract

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.