• The Transactions of the Korean Institute of Power Electronics
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• v.18 no.5
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• pp.466-476
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• 2013

This paper describes a fuzzy control method to control the output voltage of the three-phase Z-source PWM rectifier. A fuzzy control system is a control system based on fuzzy logic, and the fuzzy controller uses a single input fuzzy theory with its fuzzification. Analytical structure of the simplest fuzzy controller is derived through the triangular membership functions with its fuzzification. By setting the membership functions of the fuzzy rules, fuzzy control is achieved. The PI portion of the output DC voltage controller is controlled by fuzzy method. To confirm the validity of the proposed method, the simulation and experiment were performed, The simulation is performed with PSIM and MATLAB/SIMULINK. For the experiment, we used a DSP(TMS320F28335) controller to compute the reference value and generate the PWM pulses. For the transient state performance of the output DC voltage control of Z-source PWM rectifier, the PI controller and fuzzy controller were compared, also the conventional PWM rectifier and Z-source PWM rectifier were compared. From the results, the Z-source rectifier could allow to buck or boost of the output DC voltage. Through the analysis of the transient state, we could observe that the fuzzy controller has better performance than the conventional PI controller.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1168-1172
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• 2004

This paper presents a speed controller based on an adaptive fuzzy algorithm for high performance permanent magnet synchronous motor (PMSM) drives under parameter and load variations. In many speed tracking control systems PI controller has been used due to its simple structure and easy of design. PI controller, however, suffers from the electrical machine parameter variations and disturbances. In order to improve the tracking control performance under load variations, the PI controller parameters are modified during operation by adaptive fuzzy method. This method based on optimal fuzzy logic system has simple structure and computational simplicity. It needs only sample data which is obtained by optimal controller off-line. As the sample data implemented in the adaptive fuzzy system can be modified or extended, a flexible control system can be obtained. Simulation results show the usefulness of the proposed controller.

• Journal of the Korean Institute of Intelligent Systems
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• v.4 no.2
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• pp.35-40
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• 1994

Fuzzy logic controllers are often found to behave better than PI controllers. One of the major reasons for this is that the fuzzy logic inferences used can produce nonlinear type controllers. For some applicatioins, howeveer, linear fuzzy logic controllers also perofrm better than PI controllers. In this paper, we examine linear fuzzy logic controllers to show that the truncation effects of the fuzzy logic controllers make them perform much better than the PI controllers. In terms of a performance index we used, the truncation effects reduced the index value by up to 80% for examples we studied.

• The Transactions of the Korean Institute of Power Electronics
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• v.9 no.6
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• pp.602-611
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• 2004

Servo system has commonly adapted PI controller with fixed gains, because of its simplicity and determinative relationship among the parameters. The fixed gains PI system may be applied well to some operation conditions, but not non-linearities, complex and time variant operation conditions. For solving these problems, another conventional method, 'variable gun schedule according to speed', is published. The value of gain is determined according to the absolute value of the mover real speed. In this paper, FSTPIC(Fuzzy Self-Tuning PI Controller) is proposed based on various experiences to rapidly reduce speed error and to secure a good speed response characteristics. The effectiveness of proposed algorithms is demonstrated by comparing to two conventional gain systems via 4-quadrant operation.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.2
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• pp.215-219
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• 1998

This paper presents a practical application of fuzzy logic control to temperature control of glass melting furnace. Due to the characteristics of glass melting furnace, a hybrid algorithm of conventional PI controller and fuzzy logic controller is proposed and discussed. Practical implementation results of the production furnace showed the effectiveness of the proposed control algorithm.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.6
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• pp.767-781
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• 2013

In this paper, we proposes the AC input voltage and current sensorless control scheme to control the input power factor and DC output voltage of the three-phase Z-source PWM rectifier. For DC-link voltage control which is sensitive to the system parameters of the PWM rectifier, fuzzy-PI controller is used. Because the AC input voltage and current are estimated using only the DC-link voltage and current, AC input voltage and current sensors are not required. In addition, the unity input power factor and DC output voltage can be controlled. The phase-angle of the detected AC input voltage and estimated voltage, the response characteristics of the DC output voltage according to the DC voltage references, the FFT results of the estimated voltage and current, efficiency, and the response characteristics of the conventional PI controller and fuzzy-PI controller are verified by PSIM simulation.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.1
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• pp.34-40
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• 1994

The design and implementation of a fuzzy self-organizing controller for an environmental temperature chamber is discussed. The chamber is a non-linear, time-variant system with delay-time and dead-time. And the parameter tuning is required in PI control when the performance degraded. However the proposed fuzzy-SOC monitors the performance of the process. modifies the data base, and performs the delay-time compensation based on the idealized process model. A series of experiments was performed for the conventional PI and the fuzzy-SOC. These experimental results show the usefulness of the fuzzy-SOC.

• Proceedings of the KIEE Conference
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• 1994.11a
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• pp.165-168
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• 1994

The paper deals with the fuzzy PI control of a switched reluctance motor drive. Fuzzy algorithm based on linguistic rules describing the operator's control strategy is applied to speed control of the SRM. Simulation and experimental results show that performance of the furry PI controller is superior to that of conventional PI controller. In particular the robustness of the system is improved.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.4
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• pp.488-493
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• 2008

A mathematical model of molten steel level for continuous casting process is presented, where the molten steel level, input and output flow in the mold, the relation between stopper position and input flow etc. are considered. The mathematical model is implemented and simulated by using MATLAB. Comparing the result of molten steel level from the simulator with that of real plant, the performance of the model is shown to be reasonable. By using this simulator, it is shown that PI controller with variable P gain, adjusted by fuzzy logic system, has better control result than conventional PI controller.

• International Journal of Control, Automation, and Systems
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• v.4 no.4
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• pp.438-447
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• 2006

Fuzzy controller's design depends mainly on the rule base and membership functions over the controller's input and output ranges. This paper presents two different approaches to deal with these design issues. A simple and efficient approach; namely, Fuzzy Subtractive Clustering is used to identify the rule base needed to realize Fuzzy PI and PD type controllers. This technique provides a mechanism to obtain the reduced rule set covering the whole input/output space as well as membership functions for each input variable. But it is found that some membership functions projected from different clusters have high degree of similarity. The number of membership functions of each input variable is then reduced using a similarity measure. In this paper, the fuzzy subtractive clustering approach is shown to reduce 49 rules to 8 rules and number of membership functions to 4 and 6 for input variables (error and change in error) maintaining almost the same level of performance. Simulation on a wide range of linear and nonlinear processes is carried out and results are compared with fuzzy PI and PD type controllers without clustering in terms of several performance measures such as peak overshoot, settling time, rise time, integral absolute error (IAE) and integral-of-time multiplied absolute error (ITAE) and in each case the proposed schemes shows an identical performance.