无人拖拉机电气系统控制策略研究  

Research on Control Strategy of Electrical System of Unmanned Tractor

作　　者：陈艳茹 亓丹丹 张永学 高艳芳 Chen Yanru;Qi Dandan;Zhang Yongxue;Gao Yanfang(Huanghe Jiaotong University,Jiaozuo 454950,China)

机构地区：[1]黄河交通学院,河南焦作454950

出　　处：《农机化研究》2025年第6期258-263,共6页Journal of Agricultural Mechanization Research

基　　金：教育部产学合作协同育人项目(202101218021);河南省社科联调研项目(SKL-2021-2800)。

摘　　要：无人拖拉机成为现代农业领域的重要装备,电气系统中的执行机构是保证无人拖拉机田间正常运行的重要结构。为了进一步推动无人拖拉机的自动化及智能化发展,考虑到农田复杂的生产环境对车辆的影响,提出了一种基于RBF神经网络模型的路径跟踪器,通过将单神经网络与模糊控制相结合,实现了无人驾驶拖拉机的控制精度提升。此方法能够根据传感器获取的环境信息,通过神经网络学习和模糊控制优化,实现精确的路径跟踪。最后,通过田间实际测试验证了控制策略的有效性和性能。结果表明:基于RBF神经网络模型的路径跟踪器能够显著提高无人拖拉机的控制精度和稳定性,无人拖拉机田间行驶误差在±10cm内,转向角度≤5°。研究结果可以为无人拖拉机自动化和智能化发展提供了新的控制策略和技术支持,具有实际应用价值。Unmanned tractors have become an important piece of equipment in modern agriculture.The electrical system control strategy has an important impact on the performance and efficiency of the unmanned tractor.In order to further promote the automation and intelligent development of unmanned tractors,a path tracker based on RBF neural network model was proposed to realize the improvement of control accuracy of unmanned tractors by combining single neural network with fuzzy control,considering the influence of the complex production environment in farmland on the vehicle.The method was able to achieve accurate path tracking through neural network learning and fuzzy control optimization based on the environmental information acquired by sensors.Finally,the effectiveness and performance of the control strategy were verified by practical tests in the field.The results showed that the path tracker based on RBF neural network model can signi ficantly improve the control accuracy and stability of the unmanned tractor,and the unmanned tractor was within±10 cm of the driving error in the field and≤5°of steering angle.The research results can provide a new control strategy and technical support for the automation and intelligent development of the unmanned tractor,which had practical application value.

关 键 词：无人拖拉机 电气系统 路径跟踪器 RBF神经网络 模糊控制 

分 类 号：S219.033[农业科学—农业机械化工程]