Research on the Ant Colony Optimization Fuzzy Neural Network Control Algorithm for ABS

摘要：

　　As the convergence rate of the conventional fuzzy neural network control (FNC) algorithm for a vehicle anti-lock braking system is slow,an improved ant colony optimization fuzzy neural network control (ACO-FNC) algorithm for ABS is proposed,and the control object of ACO-FNC is slip rate.The simulation model of single-wheel ABS is established.According to the comparison of the results of the conventional FNC algorithm and ACO-FNC algorithm,the performance of ACO-FNC algorithm in convergence speed,slip ratio control quality and braking distance is better than FNC algorithm.

关键词： Ant Colony Optimization(ACO) fuzzy neural network control Anti-locked Braking System(ABS) slip rate

作者: Changping Wang Ling Wang

作者单位: College of Electrical and Information Engineering,Hunan University,Changsha 410082,China;No.95333 Tr College of Electrical and Information Engineering,Hunan University,Changsha 410082,China

会议类型: 国际会议

会议名称: Chinese Conference on Pattern Recognition, CCPR(2014年全国模式识别学术会议)

会议地点: 长沙

会议语种:英文

页码: 130-139

在线出版日期: 2014-11-01（万方平台首次上网日期，不代表论文的发表时间）