| [1] |
H. Demircioglu and P. J. Gawthrop.
Multivariable continuous-time generalised predictive control.
Automatica, 28(4):697--713, 1992.
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DOI ]
Keywords: |
| [2] |
P. J. Gawthrop, R. W. Jones, and S. A. MacKenzie.
Identification of partially-known systems.
Automatica, 28(4):831--836, 1992.
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DOI |
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The concepts of partially-known system identification are introduced and the advantages of a method whereby process information can be utilized in a linear transfer function representation of the system are highlighted and discussed. In particular, an algorithm for identifying a single unknown physical system parameter associated with a linear system is given. It is assumed that the system is polynomial (as opposed to linear) in the physical parameter. The Bond graph representation is used as the basis for deriving a system-dependent algorithm for each partially-known system. The method is illustrated by simulated examples. Keywords: |
| [3] |
P. J. Gawthrop and L. Smith.
Causal augmentation of bond graphs with algebraic loops.
Journal of the Franklin Institute, 329(2):291--303, 1992.
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DOI |
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An algorithm for the causal augmentation of bond graphs with algebraic loops is given. Unlike previous algorithms, this involves bond graph rather than equation manipulation. A number of illustrative examples are given. Keywords: 11 |
| [4] |
K. J. Hunt, R. Zbikowski, D. Sbarbaro, and P. J. Gawthrop.
Neural networks for control systems---a survey.
Automatica, 28(6):1083--1112, 1992.
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DOI |
.pdf ]
This paper focuses on the promise of artificial neural networks in the realm of modelling, identification and control of nonlinear systems. The basic ideas and techniques of artificial neural networks are presented in language and notation familiar to control engineers. Applications of a variety of neural network architectures in control are surveyed. We explore the links between the fields of control science and neural networks in a unified presentation and identify key areas for future research. Keywords: |
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