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Title: Advance control strategies for Maglev suspension systems
Author: Paddison, Jonathan Edward
Awarding Body: Loughborough University of Technology
Current Institution: Loughborough University
Date of Award: 1995
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The Birmingham Maglev developed over fifteen years ago has successfully demonstrated the inherent advantages of low speed maglev over comparable wheeled systems. It remains the only commercially operational Maglev in the world today. To develop the next generation of Maglev vehicles which will overcome some of the limitations of the Birmingham system, such as chassis length and cost, the following issues are addressed in this thesis. 1) The possibility of interaction between the chassis resonant frequencies and the suspension control system causing poor ride quality and at worst instability, are formally analysed. In the Birmingham vehicle a stiff chassis (fundamental bending mode 40Hz) is used avoiding significant interaction with the suspension controller. Using advanced control strategies the low frequency chassis resonances can be controlled allowing a vehicle structure to be used with a fundamental bending mode of about 12Hz. 2) A modem control strategy is developed which delivers an improved ride quality compared with the present classical control system despite having to operate with a 'soft' chassis. Kalman filters are digitally implemented and conclusions drawn about their performance. The classical control strategy is also successfully demonstrated on a 3 m long 'flexible beam' rig. 3) An associated Maglev suspension problem for the response to ramp inputs such as the transition onto gradients which causes either a large steady state tracking error or a worsening ride quality is addressed by modern control theory using integral feedback techniques and classical theory using third order filters. These controllers are globally optimised by a multi-objective parameter optimisation system which formally considers the conflicts inherent in a suspension system between response to stochastic inputs and deterministic inputs.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available