• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.4
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• pp.627-637
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• 1994

In this study, a predictive fuzzy control algorithm to supervise the elevator system with plural cars is developed and its performance is evaluated. The proposed algorithm is based on fuzzy in-ference system to cope with multiple control objects and uncertainty of system state. The control objects are represented as linguistic predictive fuzzy rules and simplified reasoning method is utilized as a fuzzy inference method. Real-time simulation is performed with respect o all possible modes of control, and the resultant controls ard predicted. The predicted rusults are then utilized as the control in-puts of the fuzzy rules. The feasibility of the proposed control algorithm is evaluated by graphic simulator on computer. Finallu, the results of graphic simulation is compared with those of a conventional group control algorighm.

• Journal of Power Electronics
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• v.5 no.1
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• pp.20-28
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• 2005

This paper presents a fuzzy predictive sliding mode control for high performance induction motor position drives. A new simplified inner-loop sliding-mode current control scheme based on a nonlinear mathematical model of an induction motor is introduced. Novel predictive fuzzy logic PI and PID controllers are used in speed and position loops, respectively. Sliding-mode current controllers and fuzzy predictive logic controllers are designed based on indirect vector control. The overall system performance is examined under different dynamic operating conditions. The performance of the drive system is robust and stable, and insensitive to parameters and operating condition variations even though non-exact system parameters are used in the implementation of the proposed controllers.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.778-780
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• 1996

The predictive maximum demand controllers often bring about large number of control actions during the every integrating period and/or undesirable load-disconnecting operations during the begining stage if the integrating period. To solve these problems, a fuzzy predictive maximum demand control algorithm is proposed, which determines the sensitivity if control action by urgency if the load interrupting action along with the predicted demand reading to the target or the time arriving at the end stage if the integrating period. A prototype controller employing the proposed algorithm also is developed and its performances are tested by PROCOM SYSTEMS Corperation of Korea.

• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.697-699
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• 2000

In this paper, an extension of model predictive controller for nonlinear process using Takagi-Sugeno(TS) fuzzy model is proposed Since the consequent parts of TS fuzzy model comprise linear equations of input and output variables. it is locally linear, and the Generalized Predictive Control(GPC) technique which has been developed to control Linear Time Invariant(LTI) plants, can be extended as a parallel distributed controller. Also fuzzy soft constraints are introduced to handle both equality and inequality constraints in a unified form. So the traditional constrained GPC can be transferred to a standard fuzzy optimization problem. The proposed method conciliates the advantages of the fuzzy modeling with the advantages of the constrained predictive control, and the degree of freedom is increased in specifying the desired process behavior.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.3
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• pp.272-279
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• 1997

In this paper, a fuzzy generalized predictive control (FGPC) for non-linear plants is proposed. In the proposed method, the receding horizon control is applied to the control part, while fuzzy systems are used for the predictor part. It is suggested that the fuzzy predictor is time-varying affine with respect to input variables for easy computation of control inputs. Since the receding horizon control can be obtained only with a predictor instead of a plant model, the fuzzy predictor is obtained directly from input-output data without identifying a plant model. A parameter estimation algorithm is used for identifying the fuzzy predictor. The control inputs of the FGPC are computed by minimizing a receding horizon cost function with predicted plant outputs. The proposed controller has a similar architecture to the generalized predictive control (GPC) except for the predictor synthesis method, and thus may possess inherent good properties of the GPC. Computer simulations show that the performance of the FGPC is satisfactory.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.134-138
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• 1996

Refuse incineration plant operations involve many kinds of uncertain factors, such as the variable physical properties of refuse as fuel and the complexity of the burning phenomenon. That makes it very difficult apply conventional control methods to the combustion control of the refuse. In this paper, an adaptive fuzzy model predictive controller is proposed for the combustion control of the refuse. In this paper, an adaptive fuzzy model predictive controller is proposed for the combustion control of the refuse. And computer simulation was carried out to evaluate performance of the proposed controller.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.1068-1071
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• 1993

An algorithm of fuzzy predictive sliding control is proposed to design a jet engine control system. Sliding control using predictive scheme is adopted to compensate the time delay of fuel injector. Fuzzy rule-base is also introduced to adjust the command input for suppressing the surge. The potential of the proposed algorithm is shown through simulations utilizing a typical engine-only model.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.285-289
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• 1996

Fuzzy logic controllers have been shown better performance than conventional ones especially in highly nonlinear plants. These results are caused by the nonlinear fuzzy rules were not sufficient to cope with significant uncertainty of the plants and environment. Moreover, it is hard to make fuzzy rules consistent and complete. In this paper, we employed a predictive neural network to enhance the nonlinear inference capability. The predictive neural network generates predictive outputs of a controlled plant using the current and past outputs and current inputs. These predictive outputs are used in terms of fuzzy rules in fuzzy inferencing. From experiments, we found that the predictive term of fuzzy rules enhanced the inference capability of the controller. This predictive neural network can also help the controller cope with uncertainty of plants or environment by on-line learning.

• Journal of Electrical Engineering and Technology
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• v.12 no.2
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• pp.886-889
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• 2017

An active Fault Tolerant Model Predictive Control (FTMPC) using Fuzzy scheduler is developed. Fault tolerant Control (FTC) system stages are broadly classified into two namely Fault Detection and Isolation (FDI) and fault accommodation. Basically, the faults are identified by means of state estimation techniques. Then using the decision based approach it is isolated. This is usually performed using soft computing techniques. Fuzzy Decision Making (FDM) system classifies the faults. After identification and classification of the faults, the model is selected by using the information obtained from FDI. Then this model is fed into FTC in the form of MPC scheme by Takagi-Sugeno Fuzzy scheduler. The Fault tolerance is performed by switching the appropriate model for each identified faults. Thus by incorporating the fuzzy scheduled based FTC it becomes more efficient. The system will be thereafter able to detect the faults, isolate it and also able to accommodate the faults in the sensors and actuators of the Continuous Stirred Tank Reactor (CSTR) process while the conventional MPC does not have the ability to perform it.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.5
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• pp.46-50
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• 1997

In fuzzy logic control, static fuzzy rules cannot cope with significant changes of parameters of plants or environment. To solve this prohlem, self-organizing fuzzy control. neural-network-hased fuzzy logic control and so on have heen introduced so far. However, dynamically changed fuzzy rules of these schemes may make a fuzzy logic controller Fall into dangerous situations because the changed fuzzy rules may he incomplete or inconsistent. This paper proposes a new adaptive filzzy logic control scheme using a predictivc neural network. Although some parameters of a controlled plant or environment are changed, proposed fuzzy logic controller changes its decision outputs adaptively and robustly using unchanged initial fuzzy rules and the predictive errors generated hy the predictive neural network by on-line learning. Experimental results with a D<' servo-motor position control problem show that propnsed cnntrol scheme is very useful in the viewpoint of adaptability.