Title: Feedforward fuzzy LQR path tracking controller based on predictive model and vehicle road position

Authors: Xinfeng Zhang; Zhiyuan Li; Huan Liu; Xiaorui Li; Juan Zhao

Addresses: School of Automobile, Chang'an University, Xi'an, 710064, China ' School of Automobile, Chang'an University, Xi'an, 710064, China ' School of Automobile, Chang'an University, Xi'an, 710064, China ' School of Science, Chang'an University, Xi'an, 710064, China ' School of Automobile, Chang'an University, Xi'an, 710064, China

Abstract: To improve the path tracking accuracy and ride comfort of intelligent vehicles in different scenarios, a fuzzy linear quadratic regulator (LQR) based on predictive modelling is proposed for path tracking control. A two-degree-of-freedom single trajectory model is adopted to define the vehicle tracking error in Frenet coordinate system. Predictive models are introduced to simulate the driver's behaviour to mitigate the delay of the control system. Fuzzy weight coefficient adjustment strategy is introduced to solve the problem of poor adaptability with fixed weight controller. The simulation results show that compared with the feedforward LQR controller, sliding mode control (SMC) controller and model predictive control (MPC) controller, the tracking accuracy of the enhanced LQR controller increases in three operating scenarios, and the front wheel angle and yaw rate can be steadily changed. It is proved that the improved LQR controller can enhance the track tracking accuracy and ensure the vehicle's stability and comfort.

Keywords: intelligent vehicle; path tracking; prediction model; lateral control; fuzzy LQR.

DOI: 10.1504/IJVD.2024.146772

International Journal of Vehicle Design, 2024 Vol.96 No.3/4, pp.215 - 242

Received: 01 Feb 2024
Accepted: 28 Aug 2024
Published online: 17 Jun 2025 *

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