基于人工鱼群算法-反向传播模糊神经网络的变宇宙控制方法及大型喷雾器主动悬挂性能研究

Fan Yang, Lei Liu, Yanan Zhang, Yuefeng Du, Enrong Mao, Zhongxiang Zhu, Zhen Li
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引用次数: 0

摘要

鉴于大型高空作业喷雾机的典型要求,如在恶劣路况下作业的农田植保喷雾机,以及在高作业速度下作业的喷雾机,降低喷雾机悬挂系统的振动已成为研究热点。本研究以大型高通量喷雾机的液气悬架(HPS)为研究对象,提出了一种以真实车辆振动数据为输入的变域 T-S 模糊控制器,用于实时控制悬架运动。与传统的半主动悬架不同,根据变宇宙扩展因子的特点,采用人工鱼群算法和反向传播算法相结合的训练方法,建立了精确输入的模糊神经网络控制器,对变宇宙进行优化。然后,通过模拟农田的特殊路况,分析了 HPS 的时域和频域响应特性。最后,测试了配备新控制器的喷雾器的田间性能。结果表明,AFSA-BP 算法在训练 FNN 时的误差率可降至 3.9%,与被动悬挂系统相比,T-S 模糊控制器在弹簧质量加速度、俯仰角加速度和滚动角加速度方面的效果分别提高了 18.3%、23.3% 和 27.7%,验证了本研究中主动控制器的有效性和工程实用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on a Variable Universe Control Method and the Performance of Large Sprayer Active Suspension Based on an Artificial Fish Swarm Algorithm–Back Propagation Fuzzy Neural Network
In view of the typical requirements of large high-clearance sprayers, such as those operating in poor road conditions for farmland plant protection and at high operation speeds, reducing the vibration of sprayer suspension systems has become a research hotspot. In this study, the hydro-pneumatic suspension (HPS) of large high-clearance sprayers was taken as the object, and a variable universe T-S fuzzy controller with real vehicle vibration data as input was proposed to control suspension motion in real time. Different from traditional semi-active suspension, based on the characteristics of variable universe extension factors, a training method combining the artificial fish swarm algorithm and the back propagation algorithm was used to establish a fuzzy neural network controller with precise input to optimize the variable universe. Then, the time-domain and frequency-domain response characteristics of HPS were analyzed by simulating the special road conditions typical of farmland. Finally, the field performance of the sprayer equipped with the new controller was tested. The results show that the error rate of the AFSA-BP algorithm in training the FNN could be reduced to 3.9%, and compared with a passive suspension system, the T-S fuzzy controller improved the effects of spring mass acceleration, pitch angle acceleration, and roll angle acceleration by 18.3%, 23.3%, and 27.7%, respectively, verifying the effectiveness and engineering practicality of the active controller in this study.
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