Robust Model Predictive Control for Autonomous Vehicles/Self Driving Cars

TL;DR

This paper introduces a robust MPC method for autonomous vehicle steering, enhancing control accuracy and robustness through weight tuning and online linearization techniques, with analysis of their effectiveness.

Contribution

It proposes new online linearization strategies and weight tuning methods to improve the robustness and efficiency of MPC in autonomous vehicle control.

Findings

Online linearization improves tracking accuracy.

Weight tuning enhances control robustness.

Methods balance accuracy and computational load.

Abstract

A robust Model Predictive Control (MPC) approach for controlling front steering of an autonomous vehicle is presented in this paper. We present various approaches to increase the robustness of model predictive control by using weight tuning, a successive on-line linearization of a nonlinear vehicle model to track position error and successive on-line linearization to track velocity error. Results of the effectiveness of each method in terms of accuracy and computational load are discussed.