In a recent study published in Engineering, a team of researchers from China Agricultural University and Jiangsu University has introduced a novel control mechanism for electric tractors (ETs) that significantly improves the operational quality and energy efficiency of agricultural machinery. The study, titled “Quality and Efficiency of a Brain-Smart Electric Tractor Unit Operation Control Mechanism: Instant Information Interaction and Collaborative Task Management,” presents a comprehensive approach to optimizing the performance of ETs through advanced control systems and real-time data interaction.

The research addresses the challenges faced by traditional agricultural machinery, particularly in complex terrains such as field stubble, waterlogged silt, and varying soil firmness. These conditions often lead to differentiated dynamics between the tractor and its implements, resulting in poor operational quality and low energy efficiency. To tackle these issues, the researchers propose a control mechanism that enables full lifecycle management of collaborative control tasks, instantaneous intertask interaction, and multitask synchronization.

A key innovation in this study is the development of an integrated high-performance controller structure. This controller overcomes the limitations of traditional distributed microcontrol units by providing high processing capacity and both high- and low-speed communication interfaces. The controller is designed to manage complex algorithms and facilitate high-speed, high-bandwidth communications, essential for real-time control and data processing.

The researchers also designed a hierarchical real-time operating system (ETOS) based on hierarchical stepwise control theory. This system enables preemptive kernel responses to computational tasks and competitive-collaborative synchronization among tasks. It addresses issues such as low-latency response, instantaneous information interaction, and multitask synchronization, providing robust system-level support for deep collaborative operation control.

To validate the proposed control mechanism, the researchers designed and deployed a plowing collaborative operation management strategy (COMS). This strategy dynamically and optimally controls the torque of the drive motor and plowing depth to improve plowing quality and reduce energy consumption. The COMS relies on a digital low-pass filter to manage high-frequency fluctuations in operating speed and traction resistance. It also employs Gaussian process regression to study the relationship between plowing depth and traction resistance in real time, solving a multivariate nonlinear optimal control problem iteratively using an evolutionary algorithm.

The experimental results are promising. The communication delay of collaborative tasks was as low as 83 μs, and the solution time for complex collaborative equations was as low as 46 ms. The mechanical efficiency of the ET increased by 9.07%, and the efficiency of the drive motor increased by 9.72%. The stability of the operating speed improved by 106.25%, and the stability of the plowing depth reached 94.98%.

These findings indicate that the proposed control mechanism meets the hardware and software requirements for complex collaborative control of ET units, significantly enhancing operational quality and energy efficiency. The study concludes that the application of this control mechanism has broad potential in various agricultural operations, potentially leading to substantial energy savings and improved operational quality.

The paper “Quality and Efficiency of a Brain-Smart Electric Tractor Unit Operation Control Mechanism: Instant Information Interaction and Collaborative Task Management,” is authored by Zhenhao Luo, Qingzhen Zhu, Mengnan Liu, Chunjiang Zhao, Zhenghe Song, Zhijun Meng, Bin Xie, Changkai Wen. Full text of the open access paper: https://doi.org/10.1016/j.eng.2025.02.019. For more information about Engineering, visit the website at https://www.sciencedirect.com/journal/engineering.