Pramod N. Belkhode. Analysis and Interpretation of Steering Geometry of Automobile Using Artificial Neural Network Simulation [J].Engineering,2019,(11):231-239.
Vehicle dynamics is the one of the most important factors in the analysis and predicting the steering behavior of automobile. The paper details the evaluation of the Artificial Neural Network (ANN) structures to estimate the steering geometry parameters of four wheel vehicle. One of the aspects of vehicle performance is performance of steering geometry. Steering geometry parameters kingpin inclination angle, caster angle, camber angle, toe angle, scrub radius, toe in and toe out are measured using alignment techniques and caster/camber gauges. Suspension system components pivot upon a rubber bushing which is compressed between an inner and outer metal sleeve. Excess clearance developed in the joints of suspension system in turn causes changes in steering geometry. This is obviously essential for any automobile for a major challenge in terms of operation, performance, servicing and maintenance. ANN models applicable to each of these steering parameters were developed. Steering geometry is evaluated through the independent and dependent variables of front suspension. Dependent variables such as steering geometry parameters kingpin inclination angle, caster angle, camber angle, toe angle, scrub radius, toe in and toe out are determined with the help of independent variables. These dependent variables are validated through ANN simulation. The result obtained through ANN is in close agreement to the experimental observation.
汽车动力学是汽车转向性能分析和预测的重要因素之一。详细介绍了用人工神经网络（ANN）结构估计四轮汽车转向几何参数的方法。车辆性能的一个方面是转向几何性能。转向几何参数主销内倾角、后倾角、外倾角、前束角、擦洗半径、前束内和前束外，都是使用定位技术和后倾角/外倾角测量仪测量的。悬架系统部件在橡胶衬套上转动，橡胶衬套被压缩在内外金属套筒之间。悬架系统接合处产生的过大间隙反过来会导致转向几何结构的变化。这对于任何一款在操作、性能、维修和保养方面面临重大挑战的汽车来说，显然都是必不可少的。建立了适用于这些转向参数的神经网络模型。通过前悬架的自变量和因变量计算转向几何。利用自变量确定了转向几何参数主销内倾角、后倾角、外倾角、前束角、擦洗半径、前束入、前束出。通过神经网络仿真验证了这些因变量的有效性。通过人工神经网络得到的结果与实验观测结果非常吻合。
The present methods of observing the steering parameters are not suitable and have limitation in the measurements and predicting the behavior of front suspension of an automobile [1] [2]. Steering geometry parameters kingpin inclination angle, caster angle, camber angle, toe angle, scrub radius, toe in and toe out are measured using alignment techniques and caster/camber gauges. Steering parameters change from place to place. The analysis of parameters requires a mathematical model which can usefully to the observed variation and which then provides a basis for generalization, prediction and interpretation. Steering behavior is predicated by the by the experimental investigation. Artificial Neural Network (ANN) is generally the software systems that imitate the neural network of the human brain [3]. The complex relationship between the input and output is identifying by the powerful tool of neural networks. The study indicates that the expert systems such as ANN are efficient in simulating the complicated phenomena due to its non-linear structures [4]. The objectives of this study were to evaluate the accuracy of ANN for estimation of steering parameters. Artificial Neural Network technique is recently used in the entire field to evaluate the experimental or field data. Network is trained with known inputs and outputs. Once network is trained output is predicated based on the new inputs. Paper details the validation of the experimental data with the help of Artificial Neural Network.
现有的转向参数观测方法不适用于汽车前悬架性能的测量和预测[1][2]。转向几何参数主销内倾角、后倾角、外倾角、前束角、擦洗半径、前束内和前束外，都是使用定位技术和后倾角/外倾角测量仪测量的。转向参数随地点而变化。参数分析需要一个能有效地反映观测变化的数学模型，为综合、预测和解释提供依据。通过试验研究预测了转向性能。人工神经网络（ANN）通常是模拟人脑神经网络的软件系统[3]。利用神经网络的强大工具识别输入输出之间的复杂关系。研究表明，人工神经网络等专家系统具有非线性结构，能够有效地模拟复杂现象[4]。本研究的目的是评估人工神经网路对转向参数估计的准确性。人工神经网络技术是近年来应用于整个领域的评价实验或现场数据的技术。网络通过已知的输入和输出进行训练。一旦网络被训练，输出将基于新的输入进行预测。本文利用人工神经网络对实验数据进行了验证
Joint O1 and O2 are revolute joints and joints A and B are spherical joints as shown in Figure 1. The relative orientation of two links connected at joint can be recorded in terms of value of the angles measured by potentiometer and using electronic instrumentation. At four joints (two spherical and two revolute) of the RSSR mechanism six potentiometers are located. At revolute joints O1 amp; O2 the one included angle each of these joints and at spherical joints A amp; B the two included angles at each of these joints. Once these angles are measured and position of linkage of front suspension is decided, position of kingpin axis can be located. The included angles at the joints of front suspension mechanism are first decided by potentiometers. These measured angles are supplied to interfacing program which calculates the steering performance parameters such as Kingpin angle, Camber angle, Caster angle, Toe angle, Toe in, Toe out, Scrub radius. The experimental setup is formulated on which trial are recoded with varying speed and breakers height. The steering geometry parameters such as link lengths, clearance at the joints, joints angles, breakers height, velocity and wheel diameter are recorded with the help of measuring instruments. Joints angles are measured by the potentiometer and position are joint A and B is located. Position of joint A and B further decided the position of kingpin inclination. Kingpin inclination is used for finding the steering geometry such Kingpin angle, Camber angle, Caster angle, Toe angle, Toe in, Toe out, Scrub radius.
接头O1和O2为旋转接头，接头A和B为球形接头，如图1所示。用电位差计和电子仪器测量的角度值可以记录连接在接头处的两个连杆的相对方向。在RSSR机构的四个接头（两个球形和两个旋转）处，有六个电位计。在旋转接头O1和O2处，每个接头的一个夹角；在球形接头A和B处，每个接头的两个夹角。测量这些角度并确定前悬架连杆机构的位置后，即可确定主销轴的位置。前悬架机构连接处的夹角首先由电位器决定。这些测量角度被提供给接口程序，该程序计算转向性能参数，如主销角度、外倾角、后倾角、前束角、前束内、前束外、擦洗半径。建立了不同速度和破碎机高度的试验装置。利用测量仪器记录了连杆长度、接头间隙、接头角度、断路器高度、速度和轮径等转向几何参数。接头角度由电位计测量，位置由接头A和B确定。接头A和B的位置进一步决定了主销内倾角的位置。主销内倾角用于确定主销角度、外倾角、后倾角、前束角、前束内、前束外、擦洗半径等转向几何参数。
The experimental data based modeling has been achieved based on experimental data for t
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