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ORIGINAL RESEARCH article

Front. Plant Sci.

Sec. Sustainable and Intelligent Phytoprotection

Volume 16 - 2025 | doi: 10.3389/fpls.2025.1678648

This article is part of the Research TopicAI-Driven Plant Intelligence: Bridging Multimodal Sensing, Adaptive Learning, and Ecological Sustainability in Precision Plant ProtectionView all 3 articles

Adaptive Path Tracking Control of Unmanned Agricultural Machinery with Fixed-Time Super-Twisting Sliding Mode Based on RLS-ELM

Provisionally accepted
Chen  ZhijianChen Zhijian1Yin  JianjunYin Jianjun1*Sheikh  Muhammad FarhanSheikh Muhammad Farhan2Lin  ZhenhuaLin Zhenhua1Zhou  MaileZhou Maile1
  • 1Jiangsu University School of Agricultural Engineering, Zhenjiang, China
  • 2Tongji University School of Mechanical Engineering, Shanghai, China

The final, formatted version of the article will be published soon.

Accurate path tracking is essential for achieving intelligent operation in unmanned agricultural machinery. To address the limitations of traditional agricultural machine path tracking methods, which are susceptible to high-frequency oscillations and external disturbances, this study proposes a fixed-time super-twisting sliding mode adaptive path-tracking control for unmanned agricultural vehicles. The approach utilizes a Regularized Least Squares Extreme Learning Machine (RLS-ELM) to improve robustness and adaptability under certain operating conditions. A generalized terminal sliding mode surface is first designed by incorporating both lateral and heading deviations of the vehicle. Next, a Super-Twisting Sliding Mode control law is developed to perform path tracking, while the RLS-ELM is used to estimate and compensate for unknown disturbances. The stability of the proposed control system is verified through the construction of a new Lyapunov function. The control algorithm is validated via field experiments on an agricultural platform. Results show that, compared to the Fixed-Time Generalized Terminal Super Twisting control method (FGST) and the Fixed-Time Sliding Mode Controller (FTSMC), the Extreme Learning Machine-Adaptive Fixed-Time Generalized Super-Twisting (ELM-AFGST) method reduces lateral mean absolute errors by 24.5% and 27.4%, respectively, and decreases heading mean absolute errors by 5.4% and 30.8%, respectively. These findings demonstrate that the proposed path tracking method provides a solid theoretical framework for high-precision path tracking of unmanned agricultural machines.

Keywords: Unmanned agricultural machines, Path tracking, Terminal sliding mode, Super-twisting sliding mode control, Extreme learning machine

Received: 03 Aug 2025; Accepted: 05 Sep 2025.

Copyright: © 2025 Zhijian, Jianjun, Farhan, Zhenhua and Maile. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Yin Jianjun, Jiangsu University School of Agricultural Engineering, Zhenjiang, China

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