Identification and control of four-wheel-steering vehicles based on neural network

Lu Qiang, Wang Huiyi, Guo Kong-hui
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引用次数: 6

Abstract

Vehicle dynamics are influenced by various nonlinear factors, such as tire characteristics, road conditions, etc. Hence, it is difficult to represent the vehicle dynamics by means of a two-degrees-of-freedom linear model perfectly. This paper presents a new four-wheel-steering (4WS) control system with a neural network that has the abilities of nonlinear modeling and control. A vehicle model of the RBF network is identified from the vehicle dynamics firstly. Next, the authors design a radial basis function (RBF) network controller with this vehicle model of the RBF network. The effectiveness of the proposed method is demonstrated with computer simulations.
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基于神经网络的四轮转向车辆识别与控制
车辆动力学受到各种非线性因素的影响,如轮胎特性、道路状况等。因此,用一个二自由度的线性模型来完美地表示车辆动力学是很困难的。本文提出了一种新的具有非线性建模和控制能力的神经网络四轮转向控制系统。首先从车辆动力学角度对RBF网络的车辆模型进行了识别。然后,利用RBF网络模型设计了径向基函数(RBF)网络控制器。计算机仿真验证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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