自动平衡作为新兴的动平衡技术，可实现在线振动抑制。针对非稳态过程提出一种基于自适应粒子群优化（APSO）的自动平衡控制算法，结合BP神经网络，实现一次启停机跟踪后系统的自动平衡控制，在变速模拟试验台上进行了验证。该算法以启停机过程中配重块的调整参数及工作转速作为神经网络的输入，以系统残余振动值作为网络输出，建立输入输出量间的神经网络。将网络培训后输出残余振动预测值作为目标函数，采用粒子群优化对目标函数值最小时的配重块的调整参数进行寻优。仿真及实验结果表明，APSO-BP方法在稳态与非稳态状态下皆可完成系统的自动配平，该控制策略在悬臂试验台上进行测试：变速过程中，不平衡振动幅值在14秒内下降约75%。
Abstract
As a new dynamic balancing technology, autobalancing can achieve online vibration suppression.An autobalancing control strategy based on APSO was proposed，which combined with BP neural network to achieve the autobalancing of the system by one startup and shutdown.In the process of startup and shutdown procedure, the algorithm takes the parameters of the balance block and the working speed as the neural network input.The residual vibration value of the system was used as the output.The residual vibration prediction value of the network was used as the objective function.This algorithm was used to optimize the parameters of the balance block when the objective function value reaches minimum.Simulation and experimental results shown that, the APSO-BP method can complete the balancing automatically in the steady and unsteady state.The experiment shows that, during the variable speed process, the amplitude of unbalance vibration assignment dropped by about 75% in 14 seconds.
关键词
非稳态动平衡 /
自动平衡 /
粒子群算法 /
BP神经网络
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Key words
Nonstationary Autobalancing /
Autobalancing /
Particle Swarm Optimization /
BP neural network
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