Title: Relative navigation control of articulated vehicle based on LTV-MPC

Authors: Guoxing Bai; Yu Meng; Qing Gu; Weidong Luo; Li Liu

Addresses: School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, 100083, China ' School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, 100083, China; Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, 100083, China ' School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, 100083, China ' School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, 100083, China ' School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, 100083, China

Abstract: The automation of the articulated vehicle is an important trend of the intelligent transportation system in the mining industry. And the relative navigation is a key technology to automatic vehicles. One of the challenges in the relative navigation of the articulated vehicle is that the system constraints may cause the actuator saturation and other problems. In order to solve these problems, this paper presents a relative navigation control method based on linear time varying model predictive control (LTV-MPC). This control method can explicitly take the system constraints into account. The performance of the controller is verified by simulation and experiment. The results show that the articulated vehicle with the LTV-MPC controller can track the target heading direction, the steady-state error is less than 0.3 rad. The relative navigation controller based on LTV-MPC can be used to improve the automatic driving performance of the articulated vehicle.

Keywords: articulated vehicle; relative navigation; motion control; LTV-MPC; linear time varying model predictive control; nonlinear systems.

DOI: 10.1504/IJHVS.2021.114410

International Journal of Heavy Vehicle Systems, 2021 Vol.28 No.1, pp.34 - 54

Received: 09 Dec 2017
Accepted: 15 Mar 2018
Published online: 21 Apr 2021 *

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