Intelligent Path Tracking for Single-Track Agricultural Machinery Based on Variable Universe Fuzzy Control and PSO-SVR Steering Compensation

Huanyu Liu, Zhihang Han, Junwei Bao, Jiahao Luo, Hao Yu, Shuang Wang and Xiangnan Liu ()
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Huanyu Liu: Institute of Modern Agricultural Equipment, Xihua University, Chengdu 610039, China
Zhihang Han: Institute of Modern Agricultural Equipment, Xihua University, Chengdu 610039, China
Junwei Bao: Inner Mongolia Academy of Agricultural and Animal Husbandry Sciences, Hohhot 010000, China
Jiahao Luo: Institute of Modern Agricultural Equipment, Xihua University, Chengdu 610039, China
Hao Yu: Institute of Modern Agricultural Equipment, Xihua University, Chengdu 610039, China
Shuang Wang: Institute of Modern Agricultural Equipment, Xihua University, Chengdu 610039, China
Xiangnan Liu: Institute of Modern Agricultural Equipment, Xihua University, Chengdu 610039, China

Agriculture, 2025, vol. 15, issue 11, 1-28

Abstract: Single-track electric agricultural chassis plays a vital role in autonomous navigation and driving operations in hilly and mountainous regions, where its path tracking performance directly affects the operational accuracy and stability. However, in complex farmland environments, traditional methods often suffer from frequent turning and large tracking errors due to variable path curvature, uneven terrain, and track slippage. To address these issues, this paper proposes a path tracking algorithm combining a segmented preview model with variable universe fuzzy control, enabling dynamic adjustment of the preview distance for better curvature adaptation. Additionally, a heading deviation prediction model based on Support Vector Regression (SVR) optimized by Particle Swarm Optimization (PSO) is introduced, and a steering angle compensation controller is designed to improve the turning accuracy. Simulation and field experiments show that, compared with fixed preview distance and fixed-universe fuzzy control methods, the proposed algorithm reduces the average number of turns per control cycle by 30.19% and 18.23% and decreases the average lateral error by 34.29% and 46.96%, respectively. These results confirm that the proposed method significantly enhances path tracking stability and accuracy in complex terrains, providing an effective solution for autonomous navigation of agricultural machinery.

Keywords: electric tracked agricultural chassis; single-track braking steering; path tracking; variable universe fuzzy control; support vector regression; particle swarm optimization (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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