【摘要】 为了更精确辨识多面转子轴系的失衡参数，采用适用于复杂非线性求解问题的粒子群优化算法替代失衡参数辨识反问题求解过程。在使用粒子群优化求解时，引入混沌优化思想，分别对权重因子和迭代规律进行调整，提出了混沌权重粒子群优化（chaos weighted particle swarm optimization，简称CWPSO）和双混沌粒子群优化（double chaos particle swarm optimization，简称DCPSO），并与标准粒子群优化（standard particle swarm optimization，简称SPSO）和异步自适应粒子群优化（asynchronous adaptive particle swarm optimization，简称ASPSO）进行了仿真对比，结果显示，DCPSO的平均误差最小为2.86%，稳定性最佳。采用DCPSO在本特利RK4实验台上进行失衡参数辨识及振动抑制实验，结果表明，在转速为2 040 r/min时，该算法对多面转子轴系失衡参数辨识效果最佳，由失衡引起的振动抑制率达95%左右。更多还原

【Abstract】 In order to identify the unbalanced parameters of the multi-plane rotor shafting more accurately,the particle swarm optimization algorithm which is suitable for solving complex nonlinear problems is used to replace the inverse problem solving process of the unbalanced parameter identification. Chaos weighted particle swarm optimization(CWPSO)and double chaos particle swarm optimization(DCPSO)are proposed by introducing chaos optimization idea and adjusting the weight factor and iteration law,respectively. It is also compared with standard particle swarm optimization(SPSO)and asynchronous adaptive particle swarm optimization(ASPSO). The simulation results show that the minimum average error of DCPSO is 2.86%,and the stability is the best. The unbalance parameter identification and vibration suppression experiments are carried out on Bentley RK4 with DCPSO. The results show that the algorithm has the best identification effect for multi plane rotor shafting unbalance parameters at 2 040 r/min,and the vibration suppression rate caused by unbalance is up to 95%.更多还原