Proceedings Article10.23919/ECC.1999.7099526
Predictive control using fuzzy models — Comparative study
Jairo Espinosa,M.L. Hadjili,Vincent Wertz,Joos Vandewalle +3 more
- 01 Jan 1999
- pp 1511-1516
37
TL;DR: 4 algorithms to construct fuzzy models for Nonlinear Model Predictive Control and the comparison between the algorithms includes complexity, computational load, model representation, quality of the solution is compared.
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Abstract: Fuzzy control and predictive control techniques are two modem control strategies that have been accepted by the industry to solve complex problems. Everyday the industry demands control strategies that can deliver better performance for several operating points and these requirements have motivated the development of the theory of Nonlinear Model Predictive Control. This type of controllers can be implemented using fuzzy models. The present paper presents 4 algorithms to construct the controllers. The comparison between the algorithms includes complexity, computational load, model representation, quality of the solution. The controllers are compared using a model of a chemical process (Continuous Stirred Tank Reactor).
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Citations
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Hybrid fuzzy predictive control based on genetic algorithms for the temperature control of a batch reactor
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Transparent Fuzzy Systems in Modelling and Control
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TL;DR: This chapter deals with low-level transparency of fuzzy systems that is necessary to ensure reliable interpretation of linguistic information provided by fuzzy systems and particular attention is paid to transparency protection mechanisms for data-driven optimisation algorithms that otherwise would destroy the semantics of fuzzy system in the course of optimisation.
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Application of fuzzy model predictive control to the dissolved oxygen concentration tracking in an activated sludge process
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References
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TL;DR: An efficient method for estimating cluster centers of numerical data that can be used to determine the number of clusters and their initial values for initializing iterative optimization-based clustering algorithms such as fuzzy C-means is presented.
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Industrial applications of model based predictive control
TL;DR: Two classical applications of MBPC are described which enhance the advantages of the method: feed-forwarding, constraints handling, no-lag error on dynamic set points, easy trade-off between robustness and dynamics specifications.
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Fuzzy model-based predictive control
M.L. Hadjili,Vincent Wertz,G. Scorletti +2 more
- 16 Dec 1998
TL;DR: This work focuses on an extension of the predictive control approach to control linear time invariant plants, described by ARIMAX models, in the case when the behavior of the plant is modeled using fuzzy modeling.
20
Identification of fuzzy models for a glass furnace process
M. Hadjili,Amaury Lendasse,Vincent Wertz,S. Yurkovich +3 more
- 01 Sep 1998
TL;DR: Approaches reported on here investigate nonlinear Takagi-Sugeno (TS) fuzzy model formulations, where a linear-in-the-parameter identification problem is formulated for various combinations of measured variables and system delays.
14
Fuzzy modeling and identification. A guide for the user
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- 01 Jan 1997
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