NEO-Fuzzy State-Space Predictive Control

Yancho V. Todorov, Margarita N. Terziyska, Michail G. Petrov

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)

Abstract

This paper describes the development of a novel state-space model predictive controller. The proposed modelling structure used to capture and predict the nonlinear process dynamics lies on the concept for a neo-fuzzy neuron, deployed in state-space. The introduced approach represents a set of simple fuzzy inferences along the temporal behaviour of each input node, whose dynamics is expressed as a singleton function. The learning algorithm for the proposed modelling structure is realized as a gradient descent procedure. On the basis of the obtained neo-fuzzy state-space model, a fuzzy predictor for the purpose of predictive control is developed. The achieved predictions are used to optimize the future system response by implementing a quadratic programming optimization procedure along the stated controller horizons. The potentials of the proposed approach are studied by simulation experiments to modelling and control of a nonlinear drying plant.

Original languageEnglish
Pages (from-to)99-104
Number of pages6
JournalIFAC-PapersOnLine
Volume48
Issue number24
DOIs
Publication statusPublished - 1 Jan 2015
MoE publication typeA1 Journal article-refereed

Fingerprint

Controllers
Quadratic programming
Fuzzy inference
Learning algorithms
Neurons
Drying
Experiments

Keywords

  • modeling
  • neo-fuzzy neuron
  • optimization
  • predictive control
  • QP
  • state-space

Cite this

Todorov, Yancho V. ; Terziyska, Margarita N. ; Petrov, Michail G. / NEO-Fuzzy State-Space Predictive Control. In: IFAC-PapersOnLine. 2015 ; Vol. 48, No. 24. pp. 99-104.
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NEO-Fuzzy State-Space Predictive Control. / Todorov, Yancho V.; Terziyska, Margarita N.; Petrov, Michail G.

In: IFAC-PapersOnLine, Vol. 48, No. 24, 01.01.2015, p. 99-104.

Research output: Contribution to journalArticleScientificpeer-review

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