Application of fuzzy control logic for dead-time processes in a glass melting furnace

Aoki, Shigeaki; Kawachi, Shunsuke; Sugeno, Michio

doi:10.1016/0165-0114(90)90200-p

Cited by 35 publications

(12 citation statements)

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“…For the cases of different du(t) and Dy(t), we can use the similar fuzzy rules to describe [12]. Then by fuzzy rules deduction and fuzzy reasoning [12,20,21], we can get the fuzzy prediction of dŷðt þ LÞ described in Table 1.…”

Section: Fuzzy Predictionmentioning

confidence: 99%

“…Then by fuzzy rules deduction and fuzzy reasoning [12,20,21], we can get the fuzzy prediction of dŷðt þ LÞ described in Table 1.…”

Section: Fuzzy Predictionmentioning

confidence: 99%

“…For the glass manufacturing process which is hard to get the model of the controlled plant, a fuzzy prediction control method is presented [12]. Neural network approach is used in [13] to on-line parameter estimation in unknown or poorly known processes with time delay.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand some researchers use intelligent control methods include neural networks and fuzzy logic, etc., to control such kind of delay systems because these intelligent methods have the abilities of learning and reasoning [12][13][14][15][16][17][18][19]. For the glass manufacturing process which is hard to get the model of the controlled plant, a fuzzy prediction control method is presented [12].…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy neural network sliding mode control for long delay time systems based on fuzzy prediction

2007

Neural Comput Applic

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Delay time, which may degrade the control performance, is frequently encountered in various control processes. The fuzzy neural network sliding mode controller (FNNSMC), which incorporates the fuzzy neural network (FNN) with the sliding mode controller (SMC), is developed to control the long delay system with unknown model based on fuzzy prediction algorithm in the paper. According to the characteristics of the long delay systems, we simulate the manual operating process and predict the delayed error and its derivative based on the information of the input and output variables of the process, and then feedback these prediction values to the FNN and train the FNN with the regulation function by the idea of sliding mode control until the better control results are obtained. The FNNSMC has more robustness due to the abilities of the learning and reasoning and can eliminate the drawbacks of the general SMC, namely the chattering in the control signal and the needing knowledge of the bounds of the disturbances and uncertainties. Simulation examples demonstrate the advantages of the proposed control scheme.

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Section: Fuzzy Predictionmentioning

confidence: 99%

“…Then by fuzzy rules deduction and fuzzy reasoning [12,20,21], we can get the fuzzy prediction of dŷðt þ LÞ described in Table 1.…”

Section: Fuzzy Predictionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fuzzy neural network sliding mode control for long delay time systems based on fuzzy prediction

2007

Neural Comput Applic

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show abstract

“…In industrial thermal processes, sometimes it is a difficult task to place the temperature sensor at a location close to the heating element, such as in different furnaces [1][2][3][4]20]. Thus due to the physical separation between the sensors and the heating element a transport-delay is introduced in the thermal process.…”

Section: Introductionmentioning

confidence: 99%