Distributed fuzzy-neural state-space predictive control

Yancho Todorov, Margarita Terziyska, Luybka Doukovska

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

3 Citations (Scopus)


This paper describes the development of nonlinear state-space predictive controller based on distributed fuzzyneural model. The presented approach assumes a state-space representation in order to obtain more compact form of the model, without statement of a great number of parameters needed to represent nonlinear relations. To increase the flexibility of the network, a set of fuzzy inferences is used to estimate the current system states, as well as to construct a simple predictor needed to update the future system behavior along the defined horizons. At each sampling period an optimization task performing Quadratic Programming minimization assuming the imposed constraints on the system parameters is solved. The performance of the proposed controller is assessed by simulation experiments in modeling and control of nonlinear systems with complicated dynamics.

Original languageEnglish
Title of host publicationProceedings of the 2015 20th International Conference on Process Control, PC 2015
EditorsM. Fikar, M. Kvasnica
PublisherIEEE Institute of Electrical and Electronic Engineers
ISBN (Print)978-1-4673-6627-4
Publication statusPublished - 28 Jul 2015
MoE publication typeA4 Article in a conference publication
Event20th International Conference on Process Control, PC 2015 - Strbske Pleso, Slovakia
Duration: 9 Jun 201512 Jun 2015


Conference20th International Conference on Process Control, PC 2015
CityStrbske Pleso


  • Distributed models
  • Fuzzy-neural networks
  • Model predictive control
  • Statespace systems


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