NEO-Fuzzy State-Space Predictive Control

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

Research output: Contribution to journalArticle in a proceedings journalScientificpeer-review

3 Citations (Scopus)


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
Issue number24
Publication statusPublished - 1 Jan 2015
MoE publication typeA4 Article in a conference publication


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


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