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Most of the existing control techniques applied to automobiles are based on a linear model of the relevant dynamic system. However, in spite of the increasing demand for more sophisticated vehicles, very little research is being directed toward understanding and predicting the nonlinear response caused by non-predictable operating conditions such as the variation of suspension characteristics due to wear and tear or the frictional coefficient of the road. In this research, we used a neural network based identification technique to predict the structural response of dynamic systems and validated the technique through comparison with experimental data obtained with a cantilever beam. This identification technique is further extended to forecast the frictional coefficient μ of roads. Numerical results indicate promising future applications.

The occurrence of various vibrations and noises in an automobile, such as idling vibration, boom noise and road noise, is greatly affected by the natural vibration modes and could be developed for controlling the body strength and weight these problems could be solved and a high-performance vehicle realised. This paper presents an analytical method developed by the authors to solve these problems and gives examples of its application. In developing this method, the problems of natural vibration mode and static stiffness control were addressed. Perturbation and sensitivity analysis methods have already been proposed for mode control. Four typical methods were examined and the best one was chosen in terms of accuracy and calculation time when handling large-scale problems. For static sensitivity analysis, we proposed a nevi method which is like natural mode sensitivity analysis.