Vehicle stability sliding mode control based on RBF neural network

摘要：

According to the nonlinear and parameter time-varying characteristics of vehicle stability control, a sliding control algorithm is proposed based on radial base function (RBF) neural network. The algorithm not only can reduce the chattering caused by the conventional sliding mode, but also improve the robust of the adaptive neural network control. The simulation results show the algorithm ensures that the car could run at the direction desired by the drivers.

关键词： radial base function neural network vehicle stability nonlinearity

作者: Zhang Jinzhu Zhang Hongtian

作者单位: The Power and energy college Harbin University of Engineering Harbin China

会议类型: 国际会议

会议名称: The 2nd IEEE International Conference on Advanced Computer Control(第二届先进计算机控制国际会议 ICACC 2010)

会议地点: 沈阳

会议语种:英文

页码: 243-246

在线出版日期: 2010-03-27（万方平台首次上网日期，不代表论文的发表时间）