Abstract
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.
Highlights
The paper details the evaluation of the Artificial Neural Network (ANN) structures to estimate the steering geometry parameters of four wheel vehicle
Artificial Neural Network (ANN) is generally the software systems that imitate the neural network of the human brain [3]
An ANN model has been developed for predicating steering behavior
Summary
The analysis of parameters requires a mathematical model which can usefully to the observed variation and which 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 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
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