Adaptive MPC for Autonomous Lane Keeping

TL;DR

This paper introduces an adaptive robust model predictive control method for lane keeping that learns and compensates for unknown steering angle offsets in real-time, improving safety and performance in complex driving scenarios.

Contribution

It develops a real-time adaptive control strategy that estimates and compensates for unknown steering biases without requiring perfect vehicle model knowledge.

Findings

Effective real-time estimation of steering offset.

Maintains safety constraints during lane curvature changes.

Applicable to complex vehicle dynamics scenarios.

Abstract

This paper proposes an Adaptive Robust Model Predictive Control strategy for lateral control in lane keeping problems, where we continuously learn an unknown, but constant steering angle offset present in the steering system. Longitudinal velocity is assumed constant. The goal is to minimize the outputs, which are distance from lane center line and the steady state heading angle error, while satisfying respective safety constraints. We do not assume perfect knowledge of the vehicle lateral dynamics model and estimate and adapt in real-time the maximum possible bound of the steering angle offset from data using a robust Set Membership Method based approach. Our approach is even well-suited for scenarios with sharp curvatures on high speed, where obtaining a precise model bias for constrained control is difficult, but learning from data can be helpful. We ensure persistent feasibility using a switching strategy during change of lane curvature. The proposed methodology is general and can be applied to more complex vehicle dynamics problems.