CBA-neural network control of a non-linear full vehicle model

In this paper, the dynamic behavior of a non-linear eight degrees of freedom vehicle model having active suspensions and passenger seat controlled by a neural network (NN) controller is examined. A robust NN structure is established by using principle design data from the Matlab diagrams of system functions. In the NN structure, Classic Back-Propagation Algorithm (CBA) is employed. The user inputs a set of x1 − x16 while the output from the NN consists of f1 − f16 non-linear functions. Further, the Permanent Magnet Synchronous Motor (PMSM) controller is also determined using the same NN structure. According to various tests of the NN structure it is demonstrated that the model is able to give highly sensitive outputs for vibration condition, even using a more restricted input data set. The non-linearity occurs due to dry friction on the dampers. The vehicle body and the passenger seat using PMSM are fully controlled at the same time. The time responses of the non-linear vehicle model due to road disturbance and the frequency responses are obtained. Finally, uncontrolled and controlled cases are compared. It is seen that seat vibrations of a non-linear full vehicle model are controlled by NN based system exactly.