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
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. [Continues.]
A Doctoral Thesis. Submitted in partial fulfilment of the requirements for the award of Doctor of Philosophy at Loughborough University.
Loughborough University, Department of Aeronautical and Automotive Engineering. Engineering and Physical Sciences Research Council (EPSRC).