NEO-Fuzzy State-Space Predictive Control

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

doi:10.1016/j.ifacol.2015.12.064

NEO-Fuzzy State-Space Predictive Control

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

Not published at VTT

Research output: Contribution to journal › Article in a proceedings journal › Scientific › peer-review

4 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 language	English
Pages (from-to)	99-104
Number of pages	6
Journal	IFAC-PapersOnLine
Volume	48
Issue number	24
DOIs	https://doi.org/10.1016/j.ifacol.2015.12.064
Publication status	Published - 1 Jan 2015
MoE publication type	A4 Article in a conference publication

Funding

The authors gratefully acknowledge the financial support provided by the Ministry of Education and Science of Bulgaria, Research Fund Project FNI I 026/ . As well, the research work reported in the paper is partly supported by the project mAConI "Advanced Computing for Innovation", grant 316087, funded by the FP7 Capacity Programme (Research Potential of Convergence Regions).

Keywords

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

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10.1016/j.ifacol.2015.12.064

Cite this

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title = "NEO-Fuzzy State-Space Predictive Control",

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.",

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AU - Petrov, Michail G.

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AB - 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.

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NEO-Fuzzy State-Space Predictive Control

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Keywords

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