• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.8
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• pp.1405-1411
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• 2007

In this paper, a new speed sensorless control based on a fuzzy compensator are proposed for the interior permanent magnet synchronous motor (IPMSM) drives. The conventional proportional plus integrate(PI) control are very sensitive to step change of the command speed, parameter variations and load disturbance. To cope with these problems of the PI control, the estimated speeds are compensated by using the fuzzy logic controller (FLC). In the FLC used by the speed compensator of the IPMSM, the system control parameters are adjusted by the fuzzy rule based system, which is a logical model of the human behavior for process control. The effectiveness of algorithm is confirmed by the experiments.

• Journal of Power Electronics
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• v.10 no.1
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• pp.65-71
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• 2010

This paper presents the design and implementation of voltage source inverter type SVPWM based speed control of an induction motor using a fuzzy PI controller. This scheme enables us to adjust the speed of the motor by controlling the frequency and amplitude of the stator voltage; the ratio of the stator voltage to the frequency should be kept constant. A model of the fuzzy control system is implemented in real time with a Xilinx FPGA XC3S 400E. It is introduced to maintain a constant speed to when the load varies.

• Proceedings of the Korean Nuclear Society Conference
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• 1996.11a
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• pp.73-78
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• 1996

In this study, we proposed a fuzzy gain scheduler with intelligent learning algorithm for a reactor control. In the proposed algorithm, we used the gradient descent method to learn the rule bases of a fuzzy algorithm. These rule bases are learned toward minimizing an objective function, which is called a performance cost function. The objective of fuzzy gain scheduler with intelligent learning algorithm is the generation of adequate gains, which minimize the error of system. The condition of every plant is generally changed as time gose. That is, the initial gains obtained through the analysis of system are no longer suitable for the changed plant. And we need to set new gains, which minimize the error stemmed from changing the condition of a plant. In this paper, we applied this strategy for reactor control of nuclear power plant (NPP), and the results were compared with those of a simple PI controller, which has fixed gains. As a result, it was shown that the proposed algorithm was superior to the simple PI controller.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.67.1-67
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• 2001

This paper presents a neural network based self-tuning fuzzy PI-PD control scheme for robust speed control of induction motor. The PID controller is being widely used in industrial applications. When continuously used long time, the electric and mechanical parameters of induction motor change, degrading the performance of PID controller considerably. This paper re-analyzes the fuzzy controller as conventional PID controller structure, and proposes a neural network based self-tuning fuzzy PI-PD controller whose scaling factors are adjusted automatically. Proposed scheme is simple in structure and computational burden is small ...

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.58-65
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• 2003

In this paper, a new model, which is a Takagi-Sugeno fuzzy model, for mobile robot is presented. A controller, consisting of two loops the one of which is the inner state feedback loop designed for stability and the outer loop is a PI controller designed for tracking the reference input, is suggested. Because the robot dynamics is nonlinear, it requires the controller to be insensitive to the nonlinear term. To achieve this objective, the model is developed by well known T-S fuzzy model. The design algorithm of inner state-feedback loop is regional pole-placement. In this paper, regions, for which poles of the inner state feedback loop are lie in, are formulated by LMI's. By solving these LMI's, we can obtain the state feedback gains for T-S fuzzy system. And this paper shows that the PI controller is equivalent to the state feedback and the cost function for reference tracking is equivalent to the LQ(linear quadratic) cost. By using these properties, it is also shown in this paper that the PI controller can be obtained by solving the LQ problem.

• International Journal of Control, Automation, and Systems
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• v.4 no.2
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• pp.155-164
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• 2006

This paper focuses on a new optimization technique of a fuzzy logic based proportional integral (FLPI) load frequency controller by the multiple tabu search (MTS) algorithm. Conventionally, the membership functions and control rules of fuzzy logic control are obtained by trial and error method or experiences of designers. To overcome this problem, the MTS algorithm is proposed to simultaneously tune proportional integral gains, the membership functions and control rules of a FLPI load frequency controller in order to minimize the frequency deviations of the interconnected power system against load disturbances. The MTS algorithm introduces additional techniques for improvement of the search process such as initialization, adaptive search, multiple searches, crossover and restart process. Simulation results explicitly show that the performance of the proposed FLPI controller is superior to conventional PI and FLPI controllers in terms of overshoot and settling time. Furthermore, the robustness of the proposed FLPI controller under variation of system parameters and load change are higher than that of conventional PI and FLPI controllers.

• Journal of Power Electronics
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• v.9 no.1
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• pp.124-131
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• 2009

This paper presents a PWM dc-ac converter regulation using a Single Input Fuzzy PI Controller (SIFPIC). The SIFPIC is derived from the signed distanced method, which is a simplification of a conventional fuzzy controller. The simplification results in a one-dimensional rule table, that allows its control surface to be approximated by a piecewise linear relationship. The controller multi-loop structure is comprised of an outer voltage and an inner current feedback loop. To verify the performance of the SIFPIC, a low power PWM dc-ac converter prototype is constructed and the proposed control algorithm is implemented. The experimental results show that the SIFPIC performance is comparable to a conventional Fuzzy PI controller, but with a much reduced computation time.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.1
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• pp.109-115
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• 2010

A main technology of opening and closing a sluice gate is accurate synchronous and position control for the two cylinders when they are moving with the sluice gate together over 10[m]. Since the supply flow and supply pressure of cylinders are not constant and a nonlinear friction force of the piston in cylinders exists, a difference will be made between the displacement of two cylinders. This difference causes the sluice gate to deform and abrade, and even it may be out of order. In order to solve this problem we design two kinds of fuzzy PI controllers. The former is for a position control of two cylinders, the latter is for their synchronous control. We show some simulation results compare the performance of fuzzy PI controller to the conventional PID controller.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.1
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• pp.35-41
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• 2000

This paper describes the design of fuzzy digital PID controller using a simplified indirect inference method. First, the fuzzy digital PID controller is derived from the conventional continuous-time linear digital PID controller,. Then the fuzzification, control-rule base, and defuzzification using SIM in the design of the fuzzy controller are discussed in detail. The resulting controller is a discrete-time fuzzy version of the conventional PID controller, which has the same linear structure, but are nonlinear functions of the input signals. The proposed controller enhances the self-tuning control capability, particularly when the process to be controlled is nonlinear. When the SIIM is applied the fuzzy inference results can be calculated with splitting fuzzy variables into each action component and are determined as the functional form of corresponding variables. So the proposed method has the capability of the high speed inference and adapting with increasing the number of the fuzzy input variables easily. Computer simulation results have demonstrated that the proposed method provides better control performance than the one proposed by D. Misir et al.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.15 no.5
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• pp.382-388
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• 2003

Refrigerant flow rates of the multi-type air-conditioning system can be regulated by electronic expansion valves (EEV). The performance of the multi-type air-conditioning system may be improved by lowering the superheat at the compressor suction side. In this study, a superheat temperature setpoint reset algorithm was developed by using fuzzy logics, and a PI algorithm was applied to control the superheat temperature near setpoints. Experimental results showed energy savings and stable operations at a multi-type air-conditioning system. Therefore, the developed setpoint reset algorithm may be effectively used for the EEV superheat temperature control of the multi-type air-conditioning system.