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| Nonstationary autobalancing control based on adaptive particle swarm optimization |
| CHEN Lifang，CHEN Zhechao，WANG Weimin，QIN Yue，LI Zhaoju，YAN Ziwen |
| Key Lab of Engine Health Monitoring-Control and Networking of Ministry of Education, Beijing University of Chemical Technology, Beijing 100029, China |
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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.
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Received: 11 August 2017
Published: 15 December 2018
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