Gradient projection-based trajectory tracking control for automatic guided vehicle

Abstract

In order to solve the problems of long operation time for solving the trajectory tracking controller and low real-time tracking. This paper proposes an improved model predictive control algorithm. By introducing the gradient projection method (GPM), the projection direction is used as the fastest descent direction of the objective function, which is used to reduce the number of iterations of the operation. Meanwhile, this paper adds constraints and relaxation factors to improve the trajectory tracking accuracy and prevent the controller operations from falling into local optimal solutions. Simulation by Simulink, the improved model predictive control algorithm reduces the average operation time by 24.74% and the single maximum operation time by 36.04%, effectively improving the real-time trajectory tracking performance. Finally, the verification experiment of trajectory tracking control is carried out by actual vehicle, proving the method’s effectiveness.

KEYWORDS:

• Automatic guided vehicle
• trajectory tracking
• model predictive control
• gradient projection method

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data and materials availability

The datasets supporting the conclusion of this article are included within the article.

Additional information

Funding

This work was supported by Yancheng Institute of' Technology Training Program of Innovation and Entrepreneurship for Postgraduates under Grant of Technology Training Program of Innovation and Entrepreneurship for Postgraduates under Grant: [Grant Number SJCX23_XZ025]; the Natural Science Research Project for Higher Education Institutions in Jangsu Province: [Grant Number 21KJD460009].

Notes on contributors

Wei Liu

Wei Liu received his PhD from Nanjing University of Science and Technology, China, in 2012. From 2017 to 2021, he was the Dean of Jiangsu Coastal New Energy Vehicle Research Institute of Yancheng Institute of Technology. He is currently a professor work in School of Automotive Engineering, Yancheng Institute of Technology, China. His research interests include intelligent system design and vehicle dynamics & control. E-mail: [email protected]

Rongjun Wang

Rongjun Wang received the B.S. degree from Wuhan Polytechnic university, Wuhan, China, in 2021. He is currently a master candidate at School of Automotive Engineering, Yancheng Institute of Technology, Yancheng, China. His research interests include vehicle dynamics & control and vehicle active safety control. E-mail: [email protected] (corresponding author)

Linfeng Chen

Linfeng Chen received the B.S. degree from Henan Institute of Technology, Xinxiang, China. He is currently a master candidate at School of Automotive Engineering, Yancheng Institute of Technology, Yancheng, China. His research interests include vehicle kinematics & control and vehicle active safety control. E-mail: [email protected].

Yidong Wan

Yidong Wan, graduated from Yancheng Institute of Technology with a Bachelor’s degree in Engineering in 2015 and a Master’s degree in Engineering in 2021. He is currently working as a faculty member in the School of Automotive Engineering, Yancheng Institute of Technology. His research interests include vehicle dynamics and control and autonomous driving. E-mail: [email protected].