Damping optimization of heavy-loaded anti-vibration platform based on genetic algorithm and particle swarm algorithm

Heavy-loaded Anti-Vibration Platform (AVP) is mainly used to carry high-precision instruments and equipments to isolate vibration and impact caused by uneven complex road surface, thus, providing a stable working environment. In this paper, taking the 6 DOF series AVP as the research object, the parameters of the AVP damping system are optimized by the usage of the fast elitist multi-objective genetic algorithm (NSGA_II) and multi-objective particle swarm optimization algorithm (MOPSO) to significantly improve the anti-vibration performance of AVP. The damping and stiffness parameters of the damping system are optimized by NSGA-II and MOPSO under various road surface analog signals. The optimization results show that the anti-vibration rate of the two optimization targets of vertical acceleration and vertical displacement of upper platform under different excitation can be up to 56.21%. In addition, NSGA-II is applied to optimize the damper support angle parameters of the AVP, which increases the anti-vibration rate of the two optimization targets by 7.00%. The sine excitation is verified by experiments, and the error between experiment and simulation is as small as 8.30%, thus verifying the effectiveness of optimization parameters. © 2020, Jilin University Press. All right reserved.