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Qualitative Adaptive Identification for Powertrain Systems. Powertrain Dynamic Modelling and Adaptive Identification Algorithms with Identifiability Analysis for Real-Time Monitoring and Detectability Assessment of Physical and Semi-Physical System Parameters
Souflas, Ioannis
Souflas, Ioannis
Publication Date
2015
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The University of Bradford theses are licenced under a Creative Commons Licence.
Peer-Reviewed
Open Access status
Accepted for publication
Institution
University of Bradford
Department
Faculty of Engineering and Informatics
Awarded
2015
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Abstract
A complete chain of analysis and synthesis system identification tools for detectability
assessment and adaptive identification of parameters with physical interpretation
that can be found commonly in control-oriented powertrain models is
presented. This research is motivated from the fact that future powertrain control
and monitoring systems will depend increasingly on physically oriented system
models to reduce the complexity of existing control strategies and open the
road to new environmentally friendly technologies. At the outset of this study
a physics-based control-oriented dynamic model of a complete transient engine
testing facility, consisting of a single cylinder engine, an alternating current dynamometer
and a coupling shaft unit, is developed to investigate the functional
relationships of the inputs, outputs and parameters of the system. Having understood
these, algorithms for identifiability analysis and adaptive identification of parameters with physical interpretation are proposed. The efficacy of the recommended
algorithms is illustrated with three novel practical applications. These are,
the development of an on-line health monitoring system for engine dynamometer
coupling shafts based on recursive estimation of shaft’s physical parameters, the
sensitivity analysis and adaptive identification of engine friction parameters, and
the non-linear recursive parameter estimation with parameter estimability analysis
of physical and semi-physical cyclic engine torque model parameters. The
findings of this research suggest that the combination of physics-based control oriented
models with adaptive identification algorithms can lead to the development
of component-based diagnosis and control strategies. Ultimately, this work
contributes in the area of on-line fault diagnosis, fault tolerant and adaptive control
for vehicular systems.
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Type
Thesis
Qualification name
PhD