• Proceedings of the KIEE Conference
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• 2001.07b
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• pp.567-569
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• 2001

This paper presents improved performance on the speed control of Switched Reluctance Motor(SRM) by using fuzzy logic speed controller. The nonlinear model of SRM is used and the motor used in experiment is a 6/4 SRM. In order to prove the superiority of the fuzzy logic controller, it is applied to make use of Matlab simulation program. And to implement the control method on the SRM drive. DSP(TMS320F240) based SRM speed controller is designed and fabricated. The simulation and experiment results show that FLC is effective in settling time maximum overshoot and torque ripple.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.11a
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• pp.723-724
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• 2010

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.53 no.2
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• pp.107-113
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• 2004

This paper develops a adaptive fuzzy controller based fuzzy logic control for high performance of permanent magnet synchronous motor(PMSM) drives. In the proposed system, fuzzy control is used to implement the direct controller as well as the adaptation mechanism. The operation of the direct fuzzy controller and the fuzzy logic based adaptation mechanism is studied. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the adaptive fuzzy controller is evaluated by simulation for various operating conditions. The validity of the proposed adaptive fuzzy controller is confirmed by performance results for PMSM drive system.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.1-6
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• 2008

In this paper, we present the robust fuzzy algorithm for variable speed control of wind turbines. Generally, the plants of wind turbines are consisted of complex nonlinearities, and the parameters of variable speed of wind turbines are represented as uncertain terms. For solving these complexity, we propose the robust fuzzy algorithm. At first, the exact fuzzy modeling are performed for variable speed of wind turbines. Next, we design the fuzzy controller for reanalyzed T-S fuzzy model of the wind turbines, then, we prove the stability of the plant through the Lyapunov stability theorem. At last, an example is included for visualizing the efficiency of the proposed technique.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2005.06a
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• pp.274-280
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• 2005

The system parameters of gas turbine engine tend to change remarkably in real operating condition. It means that operators have to consider environment and suitably control fuel flow. The conventional PID controller, however, can not guarantee good control performance in the aspect of system parameter change. This paper, therefore, proposes a scheme for integrating PID control and fuzzy technique to obtain the good performance of gas turbine engine speed control on the whole operating range. The effectiveness of the proposed fuzzy PID controller is verified through computer simulation.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.51 no.12
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• pp.691-696
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• 2002

This paper presents a neural network based self-tuning fuzzy PID control scheme with variable learning rate for induction motor speed control. When induction motor is continuously used long time, its electrical and mechanical Parameters will change, which degrade the Performance of PID controller considerably. This Paper re-analyzes the fuzzy controller as conventional PID controller structure, introduces a single neuron with a back-propagation learning algorithm to tune the control parameters, and proposes a variable learning rate to improve the control performance. Proposed scheme is simple in structure and computational burden is small. The simulation using Matlab/Simulink and the experiment using dSPACE(DS1102) board are performed to verify the effectiveness of the proposed scheme.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.13 no.4
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• pp.109-119
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• 1999

Switched Reluctance Motor(SRM) have been expanding gradually their awlications in the variable speed drives due to their relatively low cost, simple and robust structure, controllability and high efficiency. In this paper neural network theory is used to detemrine fuzzy-neural network controller's membership ftmctions and fuzzy rules. In addition neural network emulator is used to emulate forward dynamics of SRM and to get error signal at fuzzy-neural controller output layer. Error signal is backpropagated through neural network emulator. The backpropagated error of emulator offers the path which reforms the fuzzy-neural network controller's mmbership ftmctions and fuzzy rules. 32bit Digital Signal Processor(TMS320C31) was used to achieve the high speed control and to realize the fuzzy-neural control algorithm. Simulation and experimental results show that in the case of load variation the proposed control rrethcd was superior to a conventional rrethod in the respect of speed response.sponse.

• Proceedings of the KIEE Conference
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• 1992.07b
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• pp.1238-1241
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• 1992

The series type DC motor is normally nonlinearly modeled, but in this paper, the nonlinear model is linearized for the speed control. The proposed algorithm is constructed by the fuzzy logic controllers. Then the system is investigated for the effects of changes by the scale factor, and fuzziness of fuzzy variables.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1253-1256
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• 1996

In this paper, a new nonlinear feedback linearization control scheme for induction motors is developed. The control scheme employs a fuzzy nonlinear identification scheme based on fuzzy basis function expansion to adoptively compensate the parameter variations, i.e. rotor resistance, mutual and self inductance etc. An important feature of the proposed control scheme is to incorporate the sliding mode controller into the scheme to speed up convergence rate. Simulation tests show the robust behavior of the proposed controller in the presence of the parameter uncertainties of the machine.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.249-254
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• 2001

Abstract In this paper, we propose a method of cooperative control based on artifical intelligent system in distributed autonomous robotic system. In general, multi-agent behavior algorithm is simple and effective for small number of robots. And multi-robot behavior control is a simple reactive navigation strategy by combining repulsion from obstacles with attraction to a goal. However when the number of robot goes on increasing, this becomes difficult to be realized because multi-robot behavior algorithm provide on multiple constraints and goals in mobile robot navigation problems. As the solution of above problem, we propose an architecture of fuzzy system for each multi-robot speed control and fuzzy-neural network for obstacle avoidance. Here, we propose an architecture of fuzzy system for each multi-robot speed control and fuzzy-neural network for their direction to avoid obstacle. Our focus is on system of cooperative autonomous robots in environment with obstacle. For simulation, we divide experiment into two method. One method is motor schema-based formation control in previous and the other method is proposed by this paper. Simulation results are given in an obstacle environment and in an dynamic environment.