| Engineering Department Faculty of Science & Technology Lancaster University, Lancaster, LA1 4YR, United Kingdom |
Office Phone +44 (0) 1524 592375 Department +44 (0) 1524 593093 E-mail c.taylor AT lancaster.ac.uk |
Lancaster University, Doctor of Philosophy, 2008
Supervisor: C.J. Taylor
This thesis is concerned with
constraint handling for systems described by a Non-Minimal State Space (NMSS)
form. Such NMSS models are formulated directly from the measured input and
output signals of the controlled process, without resort to the design and
implementation of an observer. The thesis largely focuses on the application of
Model Predictive Control (MPC) methods, a very common technique for dealing with
system constraints. It is motivated by earlier research into both NMSS and MPC
systems, with features of both combined in this thesis to yield improved
control.
The main contribution lies in the development of new methods for constraint
handling of NMSS/MPC systems that contrasts with the ad hoc approach
previously used for NMSS design based on the Proportional-Integral-Plus (PIP)
algorithm. Structural aspects of NMSS/MPC design are considered, that result
from mathematical manipulation of the closed-loop block diagram or from the
definition of the state space description. The properties of these structures
are investigated to provide an insight on features of the proposed strategies.
More specifically, a Reference Governor scheme is utilised as a supervisory
controller to account for constraints. This can lead to constraint handling in
cases where a controller is already available. Furthermore, the use of an
internal model is considered in the case of the "Forward Path" NMSS/MPC
controller that is shown to have improved robustness properties in comparison to
the conventional "Feedback" structure. In contrast to existing internal model
approaches, this technique utilises the NMSS structure of the state vector and
estimates only the elements of the state vector that are related to past values
of the output. In addition, an optimal tuning technique is presented for MPC
controllers. This approach allows for multiple objectives to be specified,
whilst satisfying any system constraints. It is also shown that a specific NMSS/MPC
structure that is proposed in this thesis, namely the NMSS/MPC controller with
an integral-of-error state, provides the designer with additional freedom when
using this tuning method.
New NMSS/MPC methods are presented for both linear and non-linear systems, with
the latter case being described by State Dependent Parameter (SDP) models. The
development and analysis of MPC/SDP control in this thesis represents the first
constraint handling control system and associated stability results for this
class of non-linear models. Simulation examples are used to illustrate the
advantages and potential limitations of the various control structures in
comparison to existing solutions.
Although not directly part of the above PhD thesis, the research was extended to consider system identification methods, with application to on-line monitoring of animal health in agricultural buildings in order to improve animal welfare. The latter research was carried out in collaboration with M3-Biores at Katholieke Universiteit Leuven and the Department of Veterinary Science and Technology for Animal Safety at the University of Milan. It is common practice by veterinarians to assess cough sounds in pig houses for diagnostic purposes. Hence, this project was concerned with the development of time series methods for the automatic identification, localisation and classification of sick pig cough sounds.
Exadaktylos, V. and Taylor, C.J. (2010) Multi–objective performance optimisation for model predictive control by goal attainment, International Journal of Control, 83, 1374–1386.
Exadaktylos, V., Taylor, C.J., Wang, L. and Young, P.C. (2009) Forward path model predictive control using a non-minimal state space form, IMECHE Proceedings Journal of Systems and Control, 223, 353-369.
Exadaktylos, V., Silva, M., Ferrari, S., Guarino, M., Taylor, C.J., Aerts, J.-M. and Berckmans, D. (2008) Time-series analysis for online recognition and localization of sick pig (sus scrofa) cough sounds, Journal of the Acoustical Society of America, 124, 3803-3809.
Exadaktylos, V., Silva, M., Aerts, J.-M., Taylor, C.J. and Berckmans, D. (2008) Real-time recognition of sick pig cough sounds, Computers and Electronics in Agriculture, 63, 207-214.
Updated 7th May 2012 (C). CJT