Fuzzy predictive functional control with adaptive fuzzy model

Dovžan, Dejan; Škrjanc, Igor

doi:10.1109/icccyb.2010.5491310

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…This model was then used to control the process using fuzzy predictive functional controller. The control algorithm is detail described in (Dovžan and Škrjanc 2010) and (Š krjanc and Matko 2000). The forgetting factors were set to 0.999 and the resetting of covariance matrices was used (Å ström and Wittenmark 1995).…”

Section: Fuzzy Predictive Functional Controlmentioning

confidence: 99%

Recursive clustering based on a Gustafson–Kessel algorithm

2010

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In this paper an on-line fuzzy identification of Takagi Sugeno fuzzy model is presented. The presented method combines a recursive Gustafson-Kessel clustering algorithm and the fuzzy recursive least squares method. The on-line Gustafson-Kessel clustering method is derived. The recursive equations for fuzzy covariance matrix, its inverse and cluster centers are given. The use of the method is presented on two examples. First example demonstrates the use of the method for monitoring of the waste water treatment process and in the second example the method is used to develop an adaptive fuzzy predictive functional controller for a pH process. The results for the Mackey-Glass time series prediction are also given.

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Section: Fuzzy Predictive Functional Controlmentioning

confidence: 99%

Recursive clustering based on a Gustafson–Kessel algorithm

2010

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“…These characteristics make the temperature process difficult to achieve stability, having much overshoot and having longer settling time. Although conventional PID controllers widely used for slow processes, their performances are poor in some specific processes [3], [14][15][16][17][18][19]. FLC is one of the advanced controllers used for temperature control in different types of HE system.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Fuzzy Logic Controller for Simulated HE Temperature Control System

Ahmad

Ishak

Ismail

2015

AMR

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This paper presents the performances of an enhanced fuzzy logic controller (EFLC) for simulated Heat Exchanger (HE) temperature control system. The HE system is modeled mathematically using Energy Balance Equation and simulated using MATLAB/Simulink software. The Fuzzy Inference Structure (FIS) used was Sugeno-type. EFLC comprises of two parts which are normalized FLC part and model reference (MR) part. Both normalized and MRFLC part was using Gaussian membership function (MF) with 7x7 rule bases. Set Point (SP) tests conducted for change from 43°C to 39°C, 39°C to 35°C and 43°C to 35°C. The performances on SP tests of the FLC and proposed EFLC were compared to PID controller. The results showed that EFLC produced lower decay ratio (DR) with less oscillations, reduced undershoot (US), shorter settling time (Ts) and minimum Integral Absoluter Error (IAE) compare to FLC and PID controller.

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Fuzzy network model-based fuzzy state controller design

Kroll

Bernd²,

Trott

2000

IEEE Trans. Fuzzy Syst.

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The performance of model-based controller design relies heavily on the quality and suitability of the utilized process model. This contribution proposes a fuzzy network based nonlinear controller design methodology. Fuzzy networks are a model approach combining high approximation quality with high interpretability. The input/output (I/O) models commonly used for identification are transformed to fuzzy state-space models. Transferring and adjusting methods from linear state-space theory a control concept consisting of a fuzzy state controller and an adaptive set-point filter for nonlinear dynamic processes is deduced. The capability of the method is demonstrated for a hydraulic drive.

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Fuzzy predictive functional control with adaptive fuzzy model

Cited by 4 publications

References 13 publications

Recursive clustering based on a Gustafson–Kessel algorithm

Recursive clustering based on a Gustafson–Kessel algorithm

Enhanced Fuzzy Logic Controller for Simulated HE Temperature Control System

Fuzzy network model-based fuzzy state controller design

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