Path tracking of unmanned forklifts based on nonlinear model predictive control

doi:10.16731/j.cnki.1671-3133.2025.04.005

Abstract

Abstract: With the development of intelligent manufacturing,intelligent warehousing,and other industries,unmanned forklifts have received more and more extensive attention,but the current research work on unmanned forklifts mainly focuses on positioning,sensing,planning,and other technologies,and the research on path tracking control is not mature enough. At present,there are two main problems in the path tracking control of unmanned forklifts. On the one hand,the existing control methods do not consider the system constraints such as steering wheel angle constraints and steering wheel angle speed constraints,and on the other hand,the existing research work usually does not use the midpoint of the front end of the forks as the control target. To solve these problems,first, a kinematic model of unmanned forklifts with the midpoint of the front end of the forks as the control point was established based on the principle of non-complete constraints,and then a path tracking control algorithm based on Nonlinear Model Predictive Control (NMPC) was designed,which was simulated and tested by MATLAB software and compared with Stanley control and other control methods. The proposed NMPC path tracking controller,which takes the midpoint of the front end of the forks as the control point,can realize high accuracy path tracking,and the maximum value of the lateral error amplitude was 0.054 9 m and the convergence value was not more than 0.014 7 m in the simulation results of different working conditions,which can reduce the maximum value of the lateral error amplitude by 75.88 % at least and convergence value by 88.34 % at least,compared with the NMPC path tracking controller with the midpoint of the front axle as the control point and the Stanley controller,etc. In addition,the real-time performance of the proposed controller can meet the requirements of low-speed control system,and the computation time in each control cycle was less than 20 ms,which was less than 40 % of the control cycle.

Key words: intelligent manufacturing, unmanned forklift, path tracking, predictive control, control points

CLC Number:

U471.1

ZHANG Hongqiong, BAI Guoxing. Path tracking of unmanned forklifts based on nonlinear model predictive control[J]. Modern Manufacturing Engineering, 2025, 535(4): 45-52.

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