• Proceedings of the KIEE Conference
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• 1999.07f
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• pp.2742-2744
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• 1999

In this paper, an auto-tuning method for fuzzy logic controller based on the genetic algorithm is presented. In the proposed method, normalization parameters and membership function parameters of fuzzy controller are translated into binary bit-strings, which are processed by the genetic algorithm in order to be optimized for the well-chosen objective function (i.e. fitness function). To examine the validity of the proposed method. a genetic algorithm based fuzzy controller for an indirect vector control of induction motors is simulated and experiment is carried out. The simulation and experimental results show a significant enhancement in shortening development time and improving system performance over a traditional manually tuned fuzzy logic controller.

• Journal of international Conference on Electrical Machines and Systems
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• v.3 no.3
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• pp.272-275
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• 2014

An improved fuzzy Kalman filtering speed estimation scheme was proposed by means of measuring stator side voltage and current value based on vector control state equation of induction motor. The designed fuzzy adaptive controller conducted recursive online correction of measurement noise covariance matrix by monitoring the ratio of theory residuals and actual residuals to make it approach real noise level gradually, allowing the filter to perform optimal estimation to improve estimation accuracy of EKF. Meanwhile, co-simulation scheme based on MATLAB and Ansoft was proposed in order to improve simulation accuracy. Field-circuit coupling problems of induction motor under the action of vector control were solved and the parameter optimization accuracy was improved dramatically. The simulation and experimental results show that this algorithm has a strong ability to inhibit the random measurement noise. It is able to estimate motor speed accurately, and has superior static and dynamic characteristics.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1296-1301
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• 2004

Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision making in complex systems. The fuzzy -neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes learning approach of fuzzy-neural network by immune algorithm. The proposed learning model is presented in an immune based fuzzy-neural network (FNN) form which can handle linguistic knowledge by immune algorithm. The learning algorithm of an immune based FNN is composed of two phases. The first phase used to find the initial membership functions of the fuzzy neural network model. In the second phase, a new immune algorithm based optimization is proposed for tuning of membership functions and structure of the proposed model.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.4
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• pp.62-68
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• 1998

In this paper, a speed controller considering the effects of parameter variations and external disturbance for induction motor driver is designed. An proportional plus integral(P1) fuzzy controller is designed to match desired speed tracking specification. Then a robust controller using Fuzzy Weight matrix are designed that in order to reduce the effect of parameter variations caused by external disturbance. The desired speed tracking control performance of the driver is preserved under wide operating range, and also good speed performance is confirmed by the computer simulation.

• Proceedings of the KIPE Conference
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• 2001.12a
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• pp.11-14
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• 2001

Direct torque control (DTC) scheme provides a very quick torque response without the complex field-orientation block and inner current regulation loop. DTC is known as an appropriate scheme for high power induction motor drives because it can be used at lower switching frequency There are a major drawbacks with the application of DTC schemes it is large current harmonics due to flux drooping in a low speed range. In order to solve the problem, the fuzzy variable switching sector scheme are adopted in this paper. A meaningful contribution of this paper is to propose a simple realization scheme of the fuzzy variable switching sector technique. Experimental results show the effectiveness of this proposition.

• Proceedings of the KIPE Conference
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• 1998.10a
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• pp.11-16
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• 1998

In this paper, simultaneous estimation of rotor speed and time constant for a voltage source inverter (VSI) fed induction motor drive are disccussed. The theory is based on the Model Reference Adaptive System (MRAS). The identifier executes Simultaneous rotor speed and time constant so that vector control of the induction may be achieved in the rotor-flux oriented reference frame. Furthermore, to eliminate the offset error caused by the change in the stator resistance, a fuzzy resistance regulator is also designed which operates in parallel with the rotor speed and time constant identifier

• Journal of Electrical Engineering and Technology
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• v.4 no.4
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• pp.499-509
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• 2009

Electric vehicles (EV) are developing fast during this decade due to drastic issues on the protection of environment and the shortage of energy sources, so new technologies allow the development of electric vehicles (EV) by means of electric motors associated with static converters. The proposed propulsion system consists of two induction motors (IM) that ensure the drive of the two back driving wheels. The electronic differential system ensures the robust control of the vehicle behavior on the road. It also allows controlling, independently, every driving wheel to turn at different speeds in any curve. This paper presents the study of an hybrid Fuzzy-sliding mode control (SMC) strategy for the electric vehicle driving wheels, stability improvement, in which the fuzzy logic system replace the discontinuous control action of the classical SMC law. Our electric vehicle fuzzy-sliding mode control's simulated in Matlab SIMULINK environment, the results obtained present the efficiency of the proposed control with no overshoot, the rising time is perfected with good disturbances rejections comparing with the classical control law.

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2304-2306
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• 2004

This paper proposes an Intelligent Speed Estimator in order to realize the speed-sensorless vector control of an induction motor. Intelligent Speed Estimator used Model Reference Adaptive System which has Fuzzy-Neural adaptive mechanism as Speed Estimation method. The Intelligent Speed Estimator estimates the speed of an induction motor with a rotor flux of a reference model and adjustable model in MRAS. The Intelligent Speed Estimator reduces the error of the rotor flux between the voltage flux model and the current flux model using the error and the change of error as input of the Estimator. The computer simulation is executed to verify the propriety and the effectiveness of the proposed speed estimator.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.2
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• pp.125-130
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• 2001

This paper presents an indirect field-oriented (IFO) induction motor position servo drives which uses the model following adaptive controller with the artificial neural network(ANN)-based rotor resistance estimator. The model reference adaptive system(MRAS)-based 2-layer ANN estimates the rotor resistance on-line and a linear model-following position controller is designed by using the estimated the rotor resistance value. At the end, a fuzzy logic system(FLS) is added to make the position controller robust to the external disturbances and the parameter variations. The simulation results show the effectiveness of the proposed method.

• Proceedings of the KIEE Conference
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• 2006.07b
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• pp.737-738
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• 2006

This paper is proposed hybrid artificial intelligent controller for high performance of induction motor drive. The design of this algorithm based on fuzzy-neural network controller that is implemented using fuzzy control and neural network. The control performance of the hybrid artificial intelligent controller is evaluated by analysis for various operating conditions. The results of experiment prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.