| 空气悬架系统动态载荷的识别 |
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| 引用本文： | 杨 斌,唐海燕.空气悬架系统动态载荷的识别[J].农业工程学报,2010,26(3):130-134. |
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| 作者姓名： | 杨 斌 唐海燕 |
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| 作者单位： | 1. 铜陵学院机械工程系,铜陵,244000
2. 空军航空大学航空理论系,长春,130000 |
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| 基金项目： | 安徽省高校青年教师资助计划项目（2008jq1144） |
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| 摘 要： | 针对空气悬架系统主动控制中神经辨识器的离线训练问题，利用BP神经网络实现从空气悬架系统非簧载质量振动加速度空间到其动态载荷空间的映射。建立带有空气悬架系统的1/4工程车辆动态模型，通过仿真得出了工程车辆空气悬架系统的非簧载质量振动加速度和动态载荷数据，以空气悬架系统的非簧载质量振动加速度数据作为神经网络的输入，动态载荷数据作为神经网络的输出，训练BP神经网络，并对训练好的BP神经网络进行泛化能力的测试，路面输入采用幅值为0.01 m，频率为1 rad/s正弦波时，识别误差率在30%以内的点占总数的82.95%；以幅值为0.02 m，频率为2 rad/s的正弦波作为系统的路面输入，识别误差率在30%以内的点占总数的77.94%。结果表明BP神经网络能够对不同的路面输入具有较好的适应性。
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| 关 键 词： | 动态载荷，神经网络，信号分析，空气悬架 |
| 收稿时间： | 7/2/2009 12:00:00 AM |
| 修稿时间： | 2009/11/1 0:00:00 |
Identification of dynamic load of air suspension system |
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| Yang Bin,Tang Haiyan.Identification of dynamic load of air suspension system[J].Transactions of the Chinese Society of Agricultural Engineering,2010,26(3):130-134. |
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| Authors: | Yang Bin Tang Haiyan |
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| Institution: | 1. Mechanical Engineering Branch/a>;Tongling University/a>;Tongling 244000/a>;China/a>;2. Department of Aviation Theory/a>;Air Force Aviation University/a>;Changchun 130000/a>;China |
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| Abstract: | Based on training neuroidentifier in off-line way in active control of air suspension system, BP artificial neural network was applied in the research of identifying dynamic load of air suspension system by vibrant acceleration of under spring mass. A dynamic model of 1/4 engineering vehicle with air suspension system was built. Vibrant acceleration data of under spring mass and dynamic load data of engineering vehicle were acquired by simulation. Vibrant acceleration data of under spring mass were input data of BP neural network, dynamic load data were output data of BP neural network. BP neural network was trained by input and output data of air suspension system. Generalized ability of trained BP neural network was tested. Road input was sine wave that its amplitude was 0.01 m and its frequency was 1 rad/s. The percentage was 82.95% that the ratio of failure identifying points to total points less than 30%. Road input was sine wave that its amplitude was 0.02 m and its frequency was 2 rad/s. The percentage was 77.94% that the ratio of failure identifying points to total points less than 30%. The result indicates that BP neural network can adapt different road inputs. |
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| Keywords: | dynamic loads neural networks signal analysis air suspensions |
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