The application of neural networks in active suspension

This thesis considers the application of neural networks to automotive suspension systems. In particular their ability to learn non-linear feedback control relationships. The speed of processing, once trained, means that neural networks open up new opportunities and allow increased complexity in the control strategies employed. The suitability of neural networks for this task is demonstrated here using multilayer perceptron, (MLP) feed forward neural networks applied to a quarter vehicle simulation model. Initially neural networks are trained from a training data set created using a non-linear optimal control strategy, the complexity of which prohibits its direct use. They are shown to be successful in learning the relationship between the current system states and the optimal control.