YU Yu-zhen
,
REN Xin-yi
,
DU Feng-shan
,
SHI Jun-jie
钢铁研究学报(英文版)
In view of characteristics of particle swarm optimization (PSO) algorithm of fast convergence but easily falling into local optimum value, a novel improved particle swarm optimization algorithm is put forward, and it is applicable to identify parameters of hydraulic pressure system model in strip rolling process. In order to maintain population diversity and enhance global optimization capability, the algorithm is firstly improved by means of decreasing its inertia weight linearly from the maximum to the minimum and then combined with chaotic characteristics of ergodicity, randomness and sensitivity to initial value. When the improved algorithm is used to identify parameters of hydraulic pressure system, the comparison of simulation curves and measured curves indicates that the identification results are reliable and close to actual situation. A new method was provided for hydraulic AGC system model identification.
关键词:
hydraulic pressing system
,
PSO algorithm
,
chaos
,
self-adaptive
,
parameter identification
