Neuro-fuzzy hybrid control system of tank level in petroleum plant

Tani, Tetsuji; Murakoshi, S.; Umano, Motohide

doi:10.1109/91.531776

Cited by 41 publications

(16 citation statements)

References 16 publications

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“…A typical combination of these two techniques is the so-called neuro-fuzzy control, which is basically a fuzzy control augmented by neural networks to enhance its characteristics like flexibility, data processing capability, and adaptability [17], [63], [72], [90], [123], [124], [138], [163], [177], [178], [186], [187], [193], [205], [209], [217], [271], [294], [305], [306], [342]. The process of fuzzy reasoning is realized by neural networks, whose connection weights correspond to the parameters of fuzzy reasoning [38], [123], [124], [135], [187], [220], [231], [232], [264].…”

Section: Neuro-fuzzy Controlmentioning

confidence: 99%

A Survey on Analysis and Design of Model-Based Fuzzy Control Systems

Feng

2006

IEEE Trans. Fuzzy Syst.

1,505

629

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Section: Neuro-fuzzy Controlmentioning

confidence: 99%

A Survey on Analysis and Design of Model-Based Fuzzy Control Systems

Feng

2006

IEEE Trans. Fuzzy Syst.

1,505

629

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“…A constrained predictive control algorithm based on feedback linearization employed to a coupled tank apparatus has described in [4]. Intelligent controls including fuzzy logic (FL) [5][6], neural network (NN) control [7][8], and genetic algorithms (GA) [9] have also been applied to the coupled tanks system. Zumberge and Passino [10] have reported the comparison between conventional control and intelligent control applied to the process control.…”

Section: Introductionmentioning

confidence: 99%

A novel fuzzy based adaptive control of the four tank system

Prusty

Pati

Mahapatra

2015

Proceedings of the 2015 Third International Conference on Computer, Communication, Control and Information Technology (C3IT)

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The paper has analyzed the four tank system (FTS) from the mechanism modeling and has also established the nonlinear and linear mathematical model. The FTS is a typical control system with nonlinear, coupling and time delay characteristics which can be used to test the applications of different control algorithms on complex systems. The aim of the process is to keep the liquid level in the tanks at the desired values. Here, Fuzzy Modified Model Reference Adaptive Control (FMMRAC) is proposed and is applied to the tank system to test its effectiveness. The response of the FMMRAC controller is verified and is compared with other control algorithms through simulation. The performance of the closed loop system is simulated using LabVIEW software. The method used here is validated using the simulated results.

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“…These tanks are usually used as examples to check novel control algorithms, as they are simple to understand and easy to reproduce. For example, a neuro-fuzzy controller is proposed by Tani et al (1996) for controlling a buffer tank using a predictive inductive model (neural network) and fuzzy decision rules.…”

Section: Buffer Tank Controlmentioning

confidence: 99%