馈能悬架自适应离线神经网络逆控制
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国家自然科学基金资助项目(50905078)、江苏省自然科学基金资助项目(BK2012714)和江苏省普通高校研究生科研创新计划资助项目(KYLX_1022)


Adaptive Off-line Neural Network Inverse Control for Energy-harvesting Suspension
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    摘要:

    在馈能悬架系统馈能回路中引入Boost/Buck型DC—DC变换器,将DC—DC变换器的非线性数学模型描述为稳态分量和扰动分量之和,设计了自适应离线神经网络逆控制器。控制器由离线神经网络逆控制器和自适应控制器组成,离线神经网络逆控制器产生直流变换器的稳态输入,并利用变换器的稳态分量产生训练离线神经网络逆控制器的样本,自适应控制器产生补偿系统参数变化和不确定性扰动的动态输入。在此基础上,对控制器的性能进行了快速控制原型试验验证。最后,对馈能悬架进行了仿真研究。结果表明,自适应离线神经网络逆控制器能够使电动机电磁阻尼力较好地跟踪理想力,改善车辆的舒适性和平顺性,并有效地回收部分悬架振动能量,实现自供能控制。

    Abstract:

    The Boost/Buck converter was introduced into the energy-harvesting circuit to coordinate dynamic performance and energy-harvesting performance of energyharvesting suspension system. The non-linear mathematical model of DC—DC converter was described by the summation of steady-state components and disturbance components. Considering the mapping capability of neural network and anti-interference of FIR filter,adaptive off-line neural network inverse controller was designed. The controller was composed of off-line neural network inverse controller and adaptive controller. The off-line neural network inverse controller was used to provide steady control input with steady components of DC—DC converter as training samples, while the adaptive controller was used to provide compensation dynamic control generated by parameter variations and uncertain interference. On this basis, the converter sample was made and the rapid control prototyping experiment was performed to validate the performance of the controller. To meet the suspension vibration isolation performance and harvest vibration energy to a large degree, the suspension control parameters was decided. And a linear motor was tested to obtain its characteristic parameters. Simulations were applied to a two-degree of freedom energy-harvesting suspension. The results showed that the linear motor can produce the desired electromagnetic damping force with the proposed controller and improve vehicle comfort. It also indicated that self-powered control with energy harvested from suspension was attainable.

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陈龙,施德华,汪若尘,钱金刚.馈能悬架自适应离线神经网络逆控制[J].农业机械学报,2015,46(2):281-287. Chen Long, Shi Dehua, Wang Ruochen, Qian Jin’gang. Adaptive Off-line Neural Network Inverse Control for Energy-harvesting Suspension[J]. Transactions of the Chinese Society for Agricultural Machinery,2015,46(2):281-287.

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  • 收稿日期:2014-02-17
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  • 在线发布日期: 2015-02-10
  • 出版日期: 2015-02-10