Path Tracking Control of a Large Rear-Wheel–Steered Combine Harvester Using Feedforward PID and Look-Ahead Ackermann Algorithms

Autonomous driving solutions for agricultural machinery have advanced rapidly; however, large-wheeled harvesters present unique challenges compared to traditional vehicles. Specifically, the 5.4 m cutting width, 9.2 m minimum turning diameter, and rear-wheel–steered configuration demand specialized path tracking and steering methods. To address these challenges, this study developed an integrated system combining feedforward PID and Look-Ahead Ackermann (LAA) algorithms with sensors, actuators, and an embedded control platform. Field experiments indicated that the system maintained an average lateral deviation of approximately 5 cm on straight-line paths, with slightly larger errors observed only during turning or alignment maneuvers. Additionally, a “three-cut” steering method was implemented, which enhanced path tracking accuracy and prevented crop damage at headland turns. Successful field tests confirmed the robustness of the developed system, highlighting its practical potential for production-level autonomous harvesting.

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Publication Details

Journal
Agriculture
Published
2025-03-22
DOI
https://doi.org/10.3390/agriculture15070676
Citations
9
Primary Topic
Vehicle Dynamics and Control Systems
Type
article
Field-Weighted Citation Impact
6.15

Funders

Controls
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article

Path Tracking Control of a Large Rear-Wheel–Steered Combine Harvester Using Feedforward PID and Look-Ahead Ackermann Algorithms

Haihui Xu, Xi-Jiang Hu, Xinhua Wei, Qingshan Liu et al.
9 citations
Agriculture
Vehicle Dynamics and Control Systems
6.15
article

Path Tracking Control of a Large Rear-Wheel–Steered Combine Harvester Using Feedforward PID and Look-Ahead Ackermann Algorithms

Haihui Xu, Xi-Jiang Hu, Xinhua Wei, Qingshan Liu, Qi Song, Yang Zhang, Shaocen Zhang
article en
9 citations

Abstract

Autonomous driving solutions for agricultural machinery have advanced rapidly; however, large-wheeled harvesters present unique challenges compared to traditional vehicles. Specifically, the 5.4 m cutting width, 9.2 m minimum turning diameter, and rear-wheel–steered configuration demand specialized path tracking and steering methods. To address these challenges, this study developed an integrated system combining feedforward PID and Look-Ahead Ackermann (LAA) algorithms with sensors, actuators, and an embedded control platform. Field experiments indicated that the system maintained an average lateral deviation of approximately 5 cm on straight-line paths, with slightly larger errors observed only during turning or alignment maneuvers. Additionally, a “three-cut” steering method was implemented, which enhanced path tracking accuracy and prevented crop damage at headland turns. Successful field tests confirmed the robustness of the developed system, highlighting its practical potential for production-level autonomous harvesting.

AgricultureVol. 15(7)
Jiangsu University (CN)
Priority Academic Program Development of Jiangsu Higher Education Institutions
Openalex Percentile: Top 4%
Vehicle Dynamics and Control Systems
6.15
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