半主动悬架山地拖拉机姿态控制系统设计与仿真
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山东校地融合项目(2020XDRHXMPT35)、国家自然科学基金项目(31971785)、中国农业大学研究生教改项目(JG2019004、JG202026、QYJC202101、JG202102)和中央高校基本科研业务费专项资金项目(2022TC053)


Attitude Control Simulation of Mountain Tractor Based on Semi-active Suspension
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    摘要:

    为提高山地拖拉机在复杂农田环境中的作业平稳性,基于Matlab/Simulink仿真平台,搭建了半主动悬架拖拉机七自由度时域仿真模型,包括四轮路面激励模型、半主动悬架振动模型、半主动悬架拖拉机车体受力分析模型、车身姿态分析模型以及半主动悬架拖拉机时域仿真模型,以车身垂向位移、车身倾斜角和车身俯仰角作为拖拉机的姿态变化参数进行仿真试验。通过构建增量式比例积分微分(Proportion integration differentiation, PID)控制器和反向传播(Back propagation, BP)神经网络PID控制器仿真模型实现对半主动悬架拖拉机车身姿态的自动控制,并分别对两种控制器的控制性能进行测试与评价。利用车身垂直向加速度和车轮相对动载作为半主动悬架系统性能的评价指标,对两种控制方式下的半主动悬架性能进行了评价。仿真结果表明:基于传统增量式PID控制算法的半主动悬架拖拉机,其车身垂直位移均方根减少42.17%、侧倾角均方根减少36.76%、俯仰角均方根减少57.85%,其车身垂向加速度为0.0177m/s2,4个车轮的动载荷均方根分别为0.0284、0.0346、0.0239、0.0304N。基于BP神经网络PID控制算法的半主动悬架拖拉机,其车身垂直位移均方根减少74.54%、侧倾角均方根减少74.66%、俯仰角均方根减少75.03%,其车身垂向加速度为7.5758×10-5m/s2,4个车轮的动载荷均方根值分别为0.0197、0.0235、0.0166、0.0198N。相比增量式PID控制的半主动悬架拖拉机,基于BP神经网络PID控制的半主动悬架拖拉机,其车体平稳性得到了较好的提高。

    Abstract:

    In order to improve the operation stability of mountain tractors in complex farmland environment, based on the Matlab/Simulink simulation platform, a time-domain simulation model of a semi-active suspension tractor with D-level road roughness as the road excitation was established and which had seven degrees of freedom. The simulation time domain model included four-wheel road excitation model, semi-active suspension vibration model, semi-active suspension vibration model, semi-active suspension tractor time domain simulation model, the vertical displacement of body, body inclination angle and body pitch angle were used as the attitude change parameters of the tractor to carry out the simulation test. By constructing the incremental proportion integration differentiation,(PID)controller and the back propagation(BP)neural network PID controller simulation model, the automatic control of the body attitude of the semi-active suspension tractor was realized, and the control performances of the two controllers were respectively evaluated. Using the vertical acceleration of the body and the relative dynamic load of the wheels as the performance evaluation indexes of the semi-active suspension system, the performance of the semi-active suspension under the two control modes was evaluated. The simulation results showed that for the semi-active suspension tractor based on the traditional incremental PID control algorithm, the RMS value of the body vertical displacement was reduced by 42.17%, the root mean square value of the roll angle was reduced by 36.76%, and the root mean square value of the pitch angle was reduced by 57.85%. The vertical acceleration of the vehicle body was 0.0177m/s2, and the RMS values of dynamic load of the four wheels were 0.0284N, 0.0346N, 0.0239N, and 0.0304N, respectively. The semi-active suspension tractor based on the BP neural network PID control algorithm reduced the RMS value of the body vertical displacement by 74.54%, the root means square value of the roll angle by 74.66%, and the root mean square value of the pitch angle by 75.03%. The vertical acceleration of the body was 7.5758×10-5m/s2, and the RMS values of dynamic load of the four wheels were 0.0197N, 0.0235N, 0.0166N and 0.0198N, respectively. Compared with the semi-active suspension tractor controlled by incremental PID, the body stability of the semi-active suspension tractor based on BP neural PID control was better improved, which provided a theoretical basis for the design of the mountain tractor control system.

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刘国辉,郝称意,李民赞,孙红.半主动悬架山地拖拉机姿态控制系统设计与仿真[J].农业机械学报,2022,53(s2):338-348. LIU Guohui, HAO Chenyi, LI Minzan, SUN Hong. Attitude Control Simulation of Mountain Tractor Based on Semi-active Suspension[J]. Transactions of the Chinese Society for Agricultural Machinery,2022,53(s2):338-348.

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  • 收稿日期:2022-06-21
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  • 在线发布日期: 2022-08-22
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