Parameter Identification of Hysteresis Model with Improved Particle Swarm Optimization
An improved particle swarm optimization (IPSO) algorithm combined with chaotic map is proposed to identify the parameters of hysteresis models. The performance of IPSO algorithm was compared with genetic algorithm (GA) in terms of the accuracy of identified parameter and the shape of the reconstructed hysteresis. Based on the IPSO, numerical simulation of a typical hysteresis model, Bouc-Wen model, with all the unknown parameters were carried out in order to show the effectiveness of the proposed approach. The results indicate that the higher quality solution than the GA method can be achieved by means of the proposed IPSO method. This may be attributed mostly to the fact that IPSO improve the global searching capability by escaping the local solutions.
Parameter Identification Hysteresis Model Particle Swarm Optimization
Meiying Ye Xiaodong Wang
Department of Physics, Zhejiang Normal University, Jinhua 321004, China Department of Electronic Engineering, Zhejiang Normal University, Jinhua 321004, China
国际会议
2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)
广西桂林
英文
415-419
2009-06-17（万方平台首次上网日期，不代表论文的发表时间）