Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.289526
Title: The application of neural networks in active suspension
Author: Fairgrieve, Andrew
ISNI:       0000 0001 3456 403X
Awarding Body: Loughborough University
Current Institution: Loughborough University
Date of Award: 2003
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Abstract:
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.
Supervisor: Not available Sponsor: Loughborough University ; Engineering and Physical Sciences Research Council (EPSRC)
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.289526  DOI: Not available
Keywords: Automatic control engineering
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