Robust nonlinear predictive controller design using Particle Swarm Optimisation (PSO): a case study in speed control of an experimental hot-rolling mill

Gaffour, Sidahmed and Mahfouf, Mahdi and Zhang, Qian and Mekki, Ibrahim El Khalil and Bouhamida, Mohamed (2012) Robust nonlinear predictive controller design using Particle Swarm Optimisation (PSO): a case study in speed control of an experimental hot-rolling mill. In: The 1st International Conference on Electrical Engineering and Control Applications, 20–22 November 2012, Khenchela, Algeria. (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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Abstract

A new design methodology for an efficient implementation of Adaptive Fuzzy Predictive Control (AFPC) using a Radial Basis Function (RBF) based neural-fuzzy model and Particle Swarm Optimisation (PSO) for an experimental hot-rolling mill is proposed in this paper. A predictive model of the process based on RBF is studied. On-line adaptive neural network identification was used to adapt the model parameters. A modified PSO is used at the optimisation process in Nonlinear Model Predictive Control (NMPC) to calculate a sequence of future control actions. The performance of AFPC along with PSO was evaluated via a computing simulation platform under different operating conditions. The simulation results demonstrate the effectiveness of the proposed control scheme Robust nonlinear predictive controller design using Partial Swarm Optimisation (PSO): a case study in speed control of an experimental hot-rolling mill (PDF Download Available). Available from: http://www.researchgate.net/publication/263844370_Robust_nonlinear_predictive_controller_design_using_Partial_Swarm_Optimisation_(PSO)_a_case_study_in_speed_control_of_an_experimental_hot-rolling_mill [accessed Sep 22, 2015].

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 > TJ Mechanical engineering and machinery > Control engineering
Divisions: Faculties > Sciences > School of Engineering and Digital Arts > Instrumentation, Control and Embedded Systems
Depositing User: Qian Zhang
Date Deposited: 18 Sep 2015 14:59 UTC
Last Modified: 22 Sep 2015 10:38 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/50536 (The current URI for this page, for reference purposes)
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