Robust Control for Lane Keeping System Using Linear Parameter Varying Approach with Scheduling Variables Reduction

TL;DR

This paper develops a robust LPV-based lane-keeping controller with reduced computational complexity using PCA, validated through CarSim simulations showing improved lateral offset control.

Contribution

It introduces a PCA-based parameter reduction for LPV models in lane-keeping, enabling efficient robust control design with validated real-world simulation results.

Findings

Significant reduction in lateral offset error

Effective parameter reduction with PCA

Validated robustness through CarSim simulations

Abstract

This paper presents a robust controller using a Linear Parameter Varying (LPV) model of the lane-keeping system with parameter reduction. Both varying vehicle speed and roll motion on a curved road influence the lateral vehicle model parameters, such as tire cornering stiffness. Thus, we use the LPV technique to take the parameter variations into account in vehicle dynamics. However, multiple varying parameters lead to a high number of scheduling variables and cause massive computational complexity. In this paper, to reduce the computational complexity, Principal Component Analysis (PCA)-based parameter reduction is performed to obtain a reduced model with a tighter convex set. We designed the LPV robust feedback controller using the reduced model solving a set of Linear Matrix Inequality (LMI). The effectiveness of the proposed system is validated with full vehicle dynamics from CarSim on an interchange road. From the simulation, we confirmed that the proposed method largely reduces the lateral offset error, compared with other controllers based on Linear Time-Invariant (LTI) system.