### Abstract

This paper presents an implicit predictive control strategy based on Intuitionistic Neo-Fuzzy predictor, as a first attempt to investigate the potentials of the intuitionistic fuzzy logic for the purpose of control applications. The proposed predictor represents a simple fuzzy-neural network as fusion from the concepts of the intuitionistic fuzzy logic, the neo-fuzzy neuron theory and the classical Takagi-Sugeno inference mechanism. The predictions are then coupled into generalized predictive control scheme where a standard quadratic control cost function is minimized over a set of predefined horizons. For simplicity, the considered process variables and the calculated output control sequence are iteratively bounded instead of explicitly constrained, in order to investigate the computational procedures related to implementation of an intuitionistic fuzzy logic. To investigate the potentials of the proposed predictive control approach, numerical experiments to control a Continuous Stirred Tank Reactor (CSTR) under uncertain conditions are studied.

Original language | English |
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Title of host publication | 2016 IEEE 8th International Conference on Intelligent Systems, IS 2016 |

Editors | Vassil Sgurev, Ronald Yager, Mincho Hadjiski, Vladimir Jotsov |

Publisher | Institute of Electrical and Electronic Engineers IEEE |

Pages | 635-640 |

ISBN (Electronic) | 978-1-5090-1354-8, 978-1-5090-1353-1 |

ISBN (Print) | 978-1-5090-1355-5 |

DOIs | |

Publication status | Published - 7 Nov 2016 |

MoE publication type | A4 Article in a conference publication |

Event | 8th IEEE International Conference on Intelligent Systems, IS 2016 - Sofia, Bulgaria Duration: 4 Sep 2016 → 6 Sep 2016 |

### Conference

Conference | 8th IEEE International Conference on Intelligent Systems, IS 2016 |
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Country | Bulgaria |

City | Sofia |

Period | 4/09/16 → 6/09/16 |

### Fingerprint

### Keywords

- Continuous Stirred Tank Reactor
- Intuitionistic Fuzzy Logic
- Intuitionistic Neo-Fuzzy Network
- Model Predictive Control
- Neo-Fuzzy Network

### Cite this

*2016 IEEE 8th International Conference on Intelligent Systems, IS 2016*(pp. 635-640). [7737494] Institute of Electrical and Electronic Engineers IEEE. https://doi.org/10.1109/IS.2016.7737494

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*2016 IEEE 8th International Conference on Intelligent Systems, IS 2016.*, 7737494, Institute of Electrical and Electronic Engineers IEEE, pp. 635-640, 8th IEEE International Conference on Intelligent Systems, IS 2016, Sofia, Bulgaria, 4/09/16. https://doi.org/10.1109/IS.2016.7737494

**Intuitionistic Neo-Fuzzy predictive control.** / Terziyska, Margarita; Todorov, Yancho.

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

TY - GEN

T1 - Intuitionistic Neo-Fuzzy predictive control

AU - Terziyska, Margarita

AU - Todorov, Yancho

PY - 2016/11/7

Y1 - 2016/11/7

N2 - This paper presents an implicit predictive control strategy based on Intuitionistic Neo-Fuzzy predictor, as a first attempt to investigate the potentials of the intuitionistic fuzzy logic for the purpose of control applications. The proposed predictor represents a simple fuzzy-neural network as fusion from the concepts of the intuitionistic fuzzy logic, the neo-fuzzy neuron theory and the classical Takagi-Sugeno inference mechanism. The predictions are then coupled into generalized predictive control scheme where a standard quadratic control cost function is minimized over a set of predefined horizons. For simplicity, the considered process variables and the calculated output control sequence are iteratively bounded instead of explicitly constrained, in order to investigate the computational procedures related to implementation of an intuitionistic fuzzy logic. To investigate the potentials of the proposed predictive control approach, numerical experiments to control a Continuous Stirred Tank Reactor (CSTR) under uncertain conditions are studied.

AB - This paper presents an implicit predictive control strategy based on Intuitionistic Neo-Fuzzy predictor, as a first attempt to investigate the potentials of the intuitionistic fuzzy logic for the purpose of control applications. The proposed predictor represents a simple fuzzy-neural network as fusion from the concepts of the intuitionistic fuzzy logic, the neo-fuzzy neuron theory and the classical Takagi-Sugeno inference mechanism. The predictions are then coupled into generalized predictive control scheme where a standard quadratic control cost function is minimized over a set of predefined horizons. For simplicity, the considered process variables and the calculated output control sequence are iteratively bounded instead of explicitly constrained, in order to investigate the computational procedures related to implementation of an intuitionistic fuzzy logic. To investigate the potentials of the proposed predictive control approach, numerical experiments to control a Continuous Stirred Tank Reactor (CSTR) under uncertain conditions are studied.

KW - Continuous Stirred Tank Reactor

KW - Intuitionistic Fuzzy Logic

KW - Intuitionistic Neo-Fuzzy Network

KW - Model Predictive Control

KW - Neo-Fuzzy Network

UR - http://www.scopus.com/inward/record.url?scp=85006049138&partnerID=8YFLogxK

U2 - 10.1109/IS.2016.7737494

DO - 10.1109/IS.2016.7737494

M3 - Conference article in proceedings

SN - 978-1-5090-1355-5

SP - 635

EP - 640

BT - 2016 IEEE 8th International Conference on Intelligent Systems, IS 2016

A2 - Sgurev, Vassil

A2 - Yager, Ronald

A2 - Hadjiski, Mincho

A2 - Jotsov, Vladimir

PB - Institute of Electrical and Electronic Engineers IEEE

ER -