Proceedings Article10.1109/FUZZY.2000.839188
Model predictive control: a data-driven approach using simple fuzzy tools
João M. C. Sousa,M. Setnes +1 more
- 07 May 2000
- Vol. 2, pp 1017-1020
5
TL;DR: Advances in model predictive control using fuzzy tools are presented and a complete approach based on data-driven fuzzy tools is applied to a small real-world process.
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Abstract: Advances in model predictive control using fuzzy tools are presented. Research results are aggregated to present a complete approach based on data-driven fuzzy tools. A fuzzy model of the system is identified from sampled data using supervised fuzzy clustering for rule extraction. This model is used in model predictive control. The non-convex optimization problem introduced by a nonlinear plant model is solved by applying discrete search techniques. The trade-off between computational time and performance that follows from the discretization is addressed by using fuzzy predictive filters. The global fuzzy predictive control approach is applied to a small real-world process.
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Citations
A data-driven predictive controller design based on reduced Hankel matrix
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TL;DR: In this article, a data-driven predictive control methodology based on reduced Hankel matrix is proposed, where the prediction is accomplished using the latent space projection of a vector of inputs onto the outputs plan, serving exactly the similar role as order reduction of state estimators.
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