Fuzzy neural network control to suppress seismic responses of railway continuous girder bridges using new magneto rheological grease damper

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Journal of Vibration and Shock ›› 2015, Vol. 34 ›› Issue (2) : 66-73.

MA Yong-quan，QIU Hong-xing

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Abstract

To solve the existing problem in the low utilization rate of the magnetic field and the control characteristic would be degraded due to the sedimentation of magneto rheological fluid for the traditional magneto rheological fluid damper (MRFD), the magneto rheological grease damper (MRGD) as a new type of damper was proposed in this paper. The neural network (NN) was employed to identify the dynamic characteristics of the full-scale MRGD, the FNN/MCO semi-active control strategy for MRGD were designed by integrating the modified clipped-optimal (MCO) algorithm into the fuzzy neral network (FNN) theory, and the SIMULINK simulation analytical platform corresponding FNN/MCO strategy was constructed. Taking the typical three-span railway continuous girder bridge as engineering background, the various evaluation criteria of this bridge with non-control, FNN/MCO semi-active control and active control based on linear quadratic gaussian (LQG) strategy were analyzed, respectively. The analytical results show that the control effects on seismic responses of bridge with FNN/MCO semi-active control strategy proposed by this paper are obviously superior to those of LQG active control strategy. Comparing with LQG strategy, the FNN/MCO strategy can more obviously contributes to the performance exert for the control devices. The both stability and robustness of FNN/MCO strategy are superior to those of LQG control strategy.

Key words

railway bridge / magneto rheological grease damper / fuzzy neural network / modified clipped-optimal algorithm / semi-active control

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MA Yong-quan;QIU Hong-xing. Fuzzy neural network control to suppress seismic responses of railway continuous girder bridges using new magneto rheological grease damper[J]. Journal of Vibration and Shock, 2015, 34(2): 66-73