A stable self-tuning fuzzy logic control system for industrial temperature regulation

Gao, Zhiqiang; Trautzsch, T.A.; Dawson, J.G.

doi:10.1109/28.993163

Cited by 48 publications

(7 citation statements)

References 14 publications

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“…(14) and (15) shows the system response when (0.5 N.m) load is applied and released for second and third order models respectively. Table (7) and (8) summarize the drop, and overshoot when load is applied and released respectively.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…For the successful design of FLC's proper selection of input and output scaling factors (SF's) and/or tuning of the other controller parameters are crucial jobs, which in many cases are done through trial and error or based on some training data [3]. Basically, the control performance of the FLCs can be enhanced by the following ways: modifying membership functions [4], inference mechanism improving [5], rule tuning [6] and scaling factors adjusting [7] [8]. Out of the various tunable parameters, SF's have the highest priority due to their global effect on the control performance.…”

Section: Introductionmentioning

confidence: 99%

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Artificial Intelligentt Method for Tuning the Output Scaling Factor of a Fuzzy Controller

Ali¹,

Hussein²,

Ismael³

2011

(AREJ)

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Scaling factor tuning is one of the most used method to enhance the performance of a fuzzy controller. This paper presents two intelligent tuning strategies to tune this factor. In the first strategy, a supervisor fuzzy controller SFC was designed to continuously adjust, on line, the scaling factor of the basic fuzzy controller BFC based on the error and change of error signals. In the second strategy, a neural network NN is used to do this task. Performance of the tuning strategies are compared with corresponding conventional fuzzy controller in terms of several performance measures such as steady state error, settling time, rising time, and peak overshoot. Simulation results show that SFC performance is better. The system implementation and tests are carried out using LabVIEW (V 8.2).

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artificial Intelligentt Method for Tuning the Output Scaling Factor of a Fuzzy Controller

Ali¹,

Hussein²,

Ismael³

2011

(AREJ)

View full text Add to dashboard Cite

show abstract

“…Researchers proposed different applications of the fuzzy controller in production processes, for example, maintaining constant temperature, water level in a tank, etc. [10][11][12]. For the first time, the fuzzy controller is proposed for the production of building materials based on fluorine anhydrite.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy batch controller for granular materials

et al. 2018

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The paper focuses on batch control of granular materials in production of building materials from fluorine anhydrite. Batching equipment is intended for smooth operation and timely feeding of supply hoppers at a required level. Level sensors and a controller of an asynchronous screw drive motor are used to control filling of the hopper with industrial anhydrite binders. The controller generates a required frequency and ensures required productivity of a feed conveyor. Mamdani-type fuzzy inference is proposed for controlling the speed of the screw that feeds mixture components. As related to production of building materials based on fluoride anhydrite, this method is used for the first time. A fuzzy controller is proven to be effective in controlling the filling level of the supply hopper. In addition, the authors determined optimal parameters of the batching process to ensure smooth operation and production of fluorine anhydrite materials of specified properties that can compete with gypsum-based products.

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“…In contrast with traditional linear and nonlinear control theory, a FLC is not based on a mathematical model and is widely used to solve problems under uncertain and vague environments, with high nonlinearities [6,7] . Since their advent, FLCs have been implemented successfully in a variety of applications such as insurance and robotics [8,9,10,11] .…”

Section: Introductionmentioning

confidence: 99%

The Advantages of PID Fuzzy Controllers Over The Conventional Types

Al-Odienat¹,

Al-Lawama²

2008

American J. of Applied Sciences

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Fuzzy logic controllers (FLC's) have the following advantages over the conventional controllers: they are cheaper to develop, they cover a wider range of operating conditions, and they are more readily customizable in natural language terms. A self-organizing fuzzy controller can automatically refine an initial approximate set of fuzzy rules. Application of PI-type fuzzy controller increases the quality factor. In this paper, the voltage raising type-pulse controller is considered. Two types of fuzzy controllers used for the control of boost converter are investigated; the simulation results confirm the above mentioned advantages. In order to prove the dynamic characteristics of the PID fuzzy controller being fast and robust, simulation studies using PSIM program are carried out and compared to the results of the conventional loop gain design method for which MATLAB program is used

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A stable self-tuning fuzzy logic control system for industrial temperature regulation

Cited by 48 publications

References 14 publications

Artificial Intelligentt Method for Tuning the Output Scaling Factor of a Fuzzy Controller

Artificial Intelligentt Method for Tuning the Output Scaling Factor of a Fuzzy Controller

Fuzzy batch controller for granular materials

The Advantages of PID Fuzzy Controllers Over The Conventional Types

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