All publications by Gawthrop in 2001

[1] Peter J Gawthrop. Control-relevant extruder modelling. In J. J. Granda and F. Cellier, editors, Proceedings of the 2001 International Conference On Bond Graph Modeling and Simulation (ICBGM'01), volume 33 of Simulation Series, pages 113--118, Phoenix Arizona, U.S.A., January 2001. Society for Computer Simulation. [ bib | .pdf ]
Plasticating extruders are an important component in the manufacture of cables and their effective control is vital to the successful dimensional control of manufactured cables.

This paper provides a physically-based model derived from the fundamental physics of the process in a form suitable for model-based control. In so doing, the power of the bond graph approach to multi-disciplinary modelling of complex systems is demonstrated.

[2] Peter J Gawthrop. Bond graphs in a behavioral context. In Norbert Giambiasi and Cluadia Frydman, editors, Proceedings of the 13th European Simulation Symposium: Simulation in Industry, pages 745--749, Marseille, France, October 2001. SCS. [ bib | .pdf ]
The relationship between the Bond Graph and the Behavioural approaches to system modelling are examined and some illustrative examples given. Because of the close links between the two approaches, it is suggested that further investigation would prove fruitful.

[3] Peter J Gawthrop, Donald J Ballance, and Dustin Vink. Bond graph based control with virtual actuators. In Norbert Giambiasi and Cluadia Frydman, editors, Proceedings of the 13th European Simulation Symposium: Simulation in Industry, pages 813--817, Marseille, France, October 2001. SCS. [ bib | .pdf ]
The concept of a virtual actuator is shown to simplify the physical-domain design of controllers for systems with non-collocated sensors and actuators.

[4] Liuping Wang and Peter J Gawthrop. On the estimation of continuous time transfer functions. Int. J. Control, 74(9):889--904, June 2001. [ bib | http | .pdf ]
This paper proposes a state variable filter approach to continuous time system identification. Two topics are studied in the paper. The first topic is related to the choice of state variable filters. The strategy we adopt is to adjust the time constants of the state variable filters so that a prediction error criterion is minimized. As a result, the estimated model reaches a balance between bias and variances shown by a simulation example. The second topic is related to the choice of model structure. We extend a multiple model estimation algorithm, developed using UD factorization, to continuous time sysem identification. The estimation algorithm generates a set of candidate models, among which the 'best' model structure is found. A simulation example is used to demonstrate the efficacy of the proposed procedure, and an industrial case study on a food cooking extrusion process is given to illustrate the applicability of the algorithm.


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