Using Adaptive Genetic Algorithm to Identify Hysteresis

摘要：

A Genetic Algorithm with Adaptive Search Space (GAASS)is proposed and applied to identify the hysteresis model parameters of an electromechanical-valve actuator installed on a pneumatic system. According to the normalized fitness distance in each generation, the proposed GAASS method consistently identifies the best search domains in the parameter space and adjusts the crossover and mutation rates in order to achieve fast convergence and high accuracy.Experiments have been conducted to investigate the effectiveness of the proposed hysteresis identification approach. The experimental results with three different types of sensors have demonstrated the effectiveness of this proposed method.

关键词： Adaptive search space Genetic algorithms Hysteresis identification

作者: Li Peng Kaifeng Lu Wen Li

作者单位: Control Science and Engineering Research Center, Southern Yangtze University Wuxi, Jiangsu, 214122 P School of Mechanical Engineering, Southern Yangtze University Wuxi, Jiangsu, 214122 P.R.China

会议类型: 国际会议

会议名称: 2006 International Symposium on Distributed Computing and Applications to Business,Engineering and Science(2006年国际电子、工程及科学领域的分布式计算应用学术研讨会)

会议地点: 杭州

会议语种:英文

页码: 513-518

在线出版日期: 2006-10-12（万方平台首次上网日期，不代表论文的发表时间）