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基于RBF神经网络的汽车ABS滑模控制器的设计 被引量：7

Slip Controller Based on RBF Neural Network for Automotive ABS
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摘要 针对汽车防抱死制动系统(ABS)在快速性及鲁棒控制方面的要求,采用基于径向基函数神经网络的方法设计了汽车ABS的滑模控制器.该方法能够削弱常规滑模控制所引起的抖动现象,也能提高单纯的神经网络自适应控制的鲁棒性能.利用MATLAB中的SIMULINK仿真工具,对车辆在干路面条件下的制动情况进行了仿真研究,验证了所设计的控制方案在汽车ABS应用中的可行性和有效性. The slip controller based on RBF neural network was designed for automotive anti-lock braking system （ABS） to meet the requirements that the braking process should be fast and robust and the chattering due to conventional slip control should be alleviated as possible. Moreover, the robustness of adaptive control system simply based on neural network can be improved to some extent if using the slip controller we designed. The simulation using the software MATLAB/SIMLILINK was done to investigate vehicles＇ braking effects on dry road pavement, thus verifying the effectiveness and feasibility of the control scheme proposed.
作者 毛艳娥 井元伟 曹一鹏 张嗣瀛
机构地区 东北大学信息科学与工程学院 沈阳航空工业学院计算机学院
出处 《东北大学学报（自然科学版）》 EI CAS CSCD 北大核心 2009年第3期309-312,共4页 Journal of Northeastern University(Natural Science)
基金 国家自然科学基金资助项目(50704012) 辽宁省博士启动基金资助项目(20061017)
关键词 防抱死制动系统 径向基函数神经网络 滑模控制 抖振 鲁棒性 ABS （anti-lock braking system） RBF （ radial basic function） neural network slip controller chattering robustness
分类号 TP13 [自动化与计算机技术—控制理论与控制工程]
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参考文献9

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二级参考文献10

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共引文献5

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引证文献7

二级引证文献9

东北大学学报（自然科学版）
2009年 第3期

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