Research of Variable Pavement Vehicle SBC Based on Adaptive RBF Neural Network Sliding Mode Control

被引：0
|
作者：
Zhou Zhiguang [1 ]
Zhang Guixiang [1 ]
机构：
[1] Hunan Univ, State Key Lab Adv Design & Mfg Vehicle Body, Coll Mech & Vehicle Engn, Changsha 410082, Hunan, Peoples R China
来源：
ICECT: 2009 INTERNATIONAL CONFERENCE ON ELECTRONIC COMPUTER TECHNOLOGY, PROCEEDINGS | 2009年
关键词：
Nonlinear system; RBF Neural Network Sliding Mode Control; Pavement Recognition; SBC; Simulation;
D O I：
10.1109/ICECT.2009.84
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Automotive SBC system is a nonlinear time-varying and uncertain system, tire character changes in the scope of large, and vehicles model is uncertain, so it is difficult to establish the precise mathematical model for non-linear vehicle braking process. Based on the basis of model parameters gaining the estimated optimal slip rate, this paper presents using adaptive RBF neural network sliding mode control algorithm in the control of variable pavement vehicle SBC, with the control of vehicle under the optimal slip rate, the simulation results show that the braking performance is very good. This shows the feasibility and validity of the adaptive RBF neural network sliding mode control algorithm presented by this paper to the vehicle SBC system.
引用
收藏
页码：536 / +
页数：2
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