Real-time implementation of new nonlinear neural adaptive generalized predictive speed control for a hot-rolling mill

Gaffour, Sid-ahmed and Mahfouf, Mahdi and Yang, Yong Y. and Gama, Miguel and Zhang, Qian (2009) Real-time implementation of new nonlinear neural adaptive generalized predictive speed control for a hot-rolling mill. In: IFAC Workshop on Automation in Mining, Mineral and Metal Industry (2009). pp. 243-248. (doi:https://doi.org/10.3182/20091014-3-CL-4011.00044) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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Official URL
http://doi.org/10.3182/20091014-3-CL-4011.00044

Abstract

The real-time application of a new design methodology for an efficient implementation of Adaptive Fuzzy Generalized Predictive Control (AFGPC) using a Radial Basis Function (RBF) based neural-fuzzy model for an experimental hot-rolling mill is presented in this paper. An optimization approach with the Gradient Decent Projection technique is proposed to calculate the predictions of the control actions. AFGPC has been implemented on a simulation platform and validated in real time to provide the mill with good speed control and regulation when steel or aluminium hot-rolling experiments are carried out. From such real time experiments and numerical simulations, it can be concluded that the proposed control scheme performs very well, showing good robustness and disturbance rejection under setpoint and load changes. These successful results will form the basis for future experiments to realise 'right first time' production of metals.

Item Type: Conference or workshop item (Paper)
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
T Technology > TA Engineering (General). Civil engineering (General) > TA168 Systems engineering, cybernetics and intelligent systems
T Technology > TA Engineering (General). Civil engineering (General) > TA 403 Materials Science
Divisions: Faculties > Sciences > School of Engineering and Digital Arts > Instrumentation, Control and Embedded Systems
Depositing User: Qian Zhang
Date Deposited: 18 Sep 2015 16:07 UTC
Last Modified: 21 Sep 2015 14:25 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/50549 (The current URI for this page, for reference purposes)
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