### Abstract

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 language | English |
---|---|

Title of host publication | Proceedings of the 2015 20th International Conference on Process Control, PC 2015 |

Editors | M. Fikar, M. Kvasnica |

Publisher | Institute of Electrical and Electronic Engineers IEEE |

Pages | 31-36 |

ISBN (Print) | 978-1-4673-6627-4 |

DOIs | |

Publication status | Published - 28 Jul 2015 |

MoE publication type | A4 Article in a conference publication |

Event | 20th International Conference on Process Control, PC 2015 - Strbske Pleso, Slovakia Duration: 9 Jun 2015 → 12 Jun 2015 |

### Conference

Conference | 20th International Conference on Process Control, PC 2015 |
---|---|

Country | Slovakia |

City | Strbske Pleso |

Period | 9/06/15 → 12/06/15 |

### Fingerprint

### Keywords

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

### Cite this

*Proceedings of the 2015 20th International Conference on Process Control, PC 2015*(pp. 31-36). [7169934] Institute of Electrical and Electronic Engineers IEEE. https://doi.org/10.1109/PC.2015.7169934

}

*Proceedings of the 2015 20th International Conference on Process Control, PC 2015.*, 7169934, Institute of Electrical and Electronic Engineers IEEE, pp. 31-36, 20th International Conference on Process Control, PC 2015, Strbske Pleso, Slovakia, 9/06/15. https://doi.org/10.1109/PC.2015.7169934

**Distributed fuzzy-neural state-space predictive control.** / Todorov, Yancho; Terziyska, Margarita; Doukovska, Luybka.

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - Distributed fuzzy-neural state-space predictive control

AU - Todorov, Yancho

AU - Terziyska, Margarita

AU - Doukovska, Luybka

PY - 2015/7/28

Y1 - 2015/7/28

N2 - 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.

AB - 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.

KW - Distributed models

KW - Fuzzy-neural networks

KW - Model predictive control

KW - Statespace systems

UR - http://www.scopus.com/inward/record.url?scp=84990946843&partnerID=8YFLogxK

U2 - 10.1109/PC.2015.7169934

DO - 10.1109/PC.2015.7169934

M3 - Conference article in proceedings

SN - 978-1-4673-6627-4

SP - 31

EP - 36

BT - Proceedings of the 2015 20th International Conference on Process Control, PC 2015

A2 - Fikar, M.

A2 - Kvasnica, M.

PB - Institute of Electrical and Electronic Engineers IEEE

ER -