Study on neural networks control algorithms for automotive adaptive suspension systems

被引：0

作者：

Fu, LJ ^{[1
]}

Cao, JG ^{[1
]}

Liao, CR ^{[1
]}

Chen, B ^{[1
]}

机构：

[1] Chongqing Inst Technol, Sch Automobile Engn, Chongqing 400050, Peoples R China

来源：

PROCEEDINGS OF THE 2005 INTERNATIONAL CONFERENCE ON NEURAL NETWORKS AND BRAIN, VOLS 1-3 | 2005年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The semi-active suspension, which consists of passive spring and active shock absorber in the light of different road conditions and automobile running conditions, is the most popular automotive suspension because active suspension is complicated in structure and passive suspension cannot meet the demands of various road conditions and automobile running conditions. In this paper, a neurofuzzy adaptive control controller via modeling of recurrent neural networks of automotive suspension is presented. The modeling of neural networks has identified automotive suspension dynamic parameters and provided learning signals to neurofuzzy adaptive control controller. In order to verify control results, a mini-bus fitted with magnetorheological fluid shock absorber and neurofuzzy control system based on DSP microprocessor has been experimented with various velocity and road surfaces. The control results have been compared with those of open loop passive suspension system. These results show that neural control algorithm exhibits good performance to reduction of mini-bus vibration.

引用

页码：1795 / 1799

页数：5

共 5 条

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[2] CHEN WW, 2003, INT J VEHICLE AUTONO, V1, P222
[3] KARNOPP D, 1995, T ASME, V117, P117
[4] An optimal self-tuning controller for an active suspension
Yu, F
Crolla, DA
[J]. VEHICLE SYSTEM DYNAMICS, 1998, 29 (01) : 51 - 65
[5] ZADEH AG, 1997, INT J VEHICLE DES, V18, P132

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