Neural networks representation of a vehicle model: 'Neuro–Vehicle (NV)'
Neural networks representation of a vehicle model: 'Neuro–Vehicle (NV)'
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DOI：
10.1504/ijvd.1996.061978
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发表时间：
2014-08
影响因子：
0.5
通讯作者：
A. Ghazizadeh;A. Fahim;M. El-Gindy
中科院分区：
文献类型：
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作者：
A. Ghazizadeh;A. Fahim;M. El-Gindy
人工神经网络（NN）领域的最新发展为车辆动力学建模提供了另一种方法，特别是在系统高度非线性的操作极限附近。本文的目的是研究称为“神经车辆”的神经网络模拟双轴车辆动态行为的能力。车辆系统的输入是前进速度和转向角，输出响应是横摆角速度、横向加速度和横向载荷传递比。为了增强神经网络的性能，神经车辆的当前和过去状态被反馈到网络。使用具有自适应学习和动量的反向传播学习规则来教授神经网络。训练数据是使用具有准静态载荷传递假设的简化非线性车辆模型生成的。这些数据涉及三种操作，每种操作以五种不同的速度进行。神经车辆的性能表明，神经网络在预测车辆的非线性行为方面速度快且相对准确。由于该方法相当新且处于早期阶段，因此将讨论与本研究相关的一些问题，并在未来的工作中尝试解决这些问题。本文描述的工作是旨在开发智能侧翻和稳定性增强警告系统的长期项目的第一阶段。
Recent developments in the area of the artificial neural networks (NN) provide an alternative approach to the modelling of vehicular dynamics, particularly near their operational limits where the system is highly nonlinear. The objective of this paper is to investigate the ability of a NN called the 'Neuro–Vehicle' to simulate the dynamic behaviour of a two–axle vehicle. The input to the vehicle system is the forward speed and the steering angle, and the output responses are the yaw rate, the lateral acceleration, and the lateral load transfer ratios. To enhance the NN performance, current and past status of the Neuro–Vehicle are fed back to the network. A back–propagation learning rule with adaptive learning and momentum was used to teach the NN. The training data were produced using a simplified non–linear vehicle model with a quasistatic load transfer assumption. These data pertain to three manoeuvres each carried out at five different speeds. The performance of the Neuro–Vehicle shows that neural networks are fast and relatively accurate in predicting the nonlinear behaviour of a vehicle. Since the approach is fairly new and in its early stage, some problems associated with this study will be discussed and attempts to solve them will appear in future work. The work described in this paper is the first stage of a long–term project aimed at developing an Intelligent Rollover and Stability Enhancement Warning System.