Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.544483
Title: Online adaptive fuzzy neural network automotive engine control
Author: James, Keith
Awarding Body: Loughborough University
Current Institution: Loughborough University
Date of Award: 2011
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Abstract:
Automotive manufacturers are investing in research and development for hybridization and more modern advanced combustion strategies. These new powertrain systems can offer the higher efficiency required to meet future emission legislation, but come at the cost of significantly increased complexity. The addition of new systems to modernise an engine increases the degrees of freedom of the control problem and the number of control variables. Advanced combustion strategies also display interlinked behaviour between control variables. This type of behaviour requires a more orchestrated multi-input multi-output control approach. Model based control is a common solution, but accurate control models can be difficult to achieve and calibrate due to the nonlinear dynamics of the engines. The modelling problem becomes worse when some advanced combustion systems display nonlinear dynamics that can change with time. Any fixed model control system would suffer from increasing model/system mismatch. Direct feedback would help reduce a degree or error from model/system mismatch, but feedback methods are often limited by cost and are generally indirect and slow response. This research addresses these problems with the development of a mobile ionisation sensor and an online adaptive control architecture for multi-input multi-output engine control. The mobile ionisation system offers a cheap, fast response, direct in-cylinder feedback for combustion control. Feedback from 30 averaged cycles can be related to combustion timing with variance as small as 0.275 crank angle degrees. The control architecture combines neural networks and fuzzy logic for the control and reduced modelling effort for complex nonlinear systems. The combined control architecture allows continuous online control adaption for calibration against model/plant mismatch and time varying dynamics. In simulation, set point tracking could be maintained for combustion timing to 4 CAD and AFR to 4, for significant dynamics shifts in plant dynamics during a transient drive cycle.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.544483  DOI: Not available
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