• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.3
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• pp.274-279
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• 2005

For high-speed fuzzy control systems, an important problem is the improvement of speed for the fuzzy inference, particularly in the consequent part and the defuzzification stage. This paper introduces an algorithm to map real values of the fuzzy membership functions in the consequent part onto an integer grid, as well as a method of eliminating the unnecessary operations of the zero items in the defuzzification stage, allowing a center of gravity method to be implemented with only integer additions and one integer division. A VHDL implementation of the system is presented. The proposed system shows approximately an order of magnitude increase in speed as compared with conventional methods while introducing only a minimal error and can be used in many fuzzy controller applications.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.8
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• pp.2062-2072
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• 1994

This paper presents the development of a fuzzy controller for driving camera pan/tilt device so that the camera's viewing direction can automatically track a moving object. To achieve computational efficiency a non-contact type displacement follower is used as a feedback sensor instead of a vision camera. The displacement follower, however, is extremely sensitive to object's lighting condition and results in unstable response at high speed. To this end, a fuzzy controller is developed in such a way to provide stable tracking performance at high speed where the sensory signal is subjected to intermittant disturbances of large magnitude. The test result shows stable tracking response even for high speed and non-uniform lighting condition. The resulting camera autotracking system can be adopted as an effective tool for visual transfer in the context of teleoperation and autonomous robotics.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.54 no.1
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• pp.8-14
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• 2005

This paper is proposed hybrid artificial intelligent(HAI) controller for speed control of surface permanent magnet synchronous motor(SPMSM) drive. The design of this algorithm based on HAI controller that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. 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 HAI controller is evaluated by analysis for various operating conditions. The results of analysis prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.138-138
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• 2000

In this paper, a methodology for designing a controller based on inverse dynamics for speed control of DC motors is presented. The proposed controller consists of a prefilter, the inverse dynamic model of a system and a fuzzy logic controller. The prefilter prevents high frequency effects from the inverse dynamic model. The model of the system is characterized by a nonlinear equation with coulomb friction. The fuzzy logic controller regulates the error between the set-point and the system output which may be caused by disturbances and it simultaneously traces the change o( the reference input. The parameters of the model are estimated by a genetic a]gorithm. An experimental work on a DC motor system is carried out to illustrate the performance of the proposed controller

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2000.11a
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• pp.193-198
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• 2000

Switched reluctance motor drives have been finding their applications in the variable speed drives due to their relatively low cost, simple and robust structure, controllability and high efficiency. Fuzzy control does not need any model of plant. It is based on plant operator experience and heuristics. Fuzzy control is basically adaptive and gives robust performance for plant parameter variation. This paper deals with the sped control of switched reluctance motor using fuzzy controller with 7-rule based fuzzy logic. The proposed fuzzy controller is superior to the control performance of the conventional PI controller. The fuzzy controller is implemented by 80C196KC, 16 bit one-chip microcontroller.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.9
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• pp.1700-1707
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• 2011

Interior permanent magnet synchronous motor(IPMSM) adjustable speed drives offer significant advantages over induction motor drives in a wide variety of industrial applications such as high power density, high efficiency, improved dynamic performance and reliability. Since the fuzzy neural network(FNN) is recognized general approximate method to control non-linearities and uncertainties, the development of FNN control systems have also grown rapidly. The FNN controller is compounded of fuzzy and neural network. It has an advantage that is the robustness of fuzzy control and the ability to adapt of neural network. However, the FNN has static problem due to their feed-forward network structure. This paper proposes high performance speed control of IPMSM drive using the recurrent FNN(RFNN) which improved conventional FNN controller. The RFNN has excellent dynamic response characteristics because of it has internally feed-back structure. Also, this paper proposes speed estimation of IPMSM drive using ANN. The proposed method is analyzed and compared to conventional FNN controller in various operating condition such as parameter variation, steady and transient states etc.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.58 no.3
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• pp.249-256
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• 2009

The widely used control theory based design of PI family controllers fails to perform satisfactorily under parameter variation, nonlinear or load disturbance. In high performance applications, it is useful to automatically extract the complex relation that represent the drive behaviour. The use of learning through example algorithms can be a powerful tool for automatic modelling variable speed drives. They can automatically extract a functional relationship representative of the drive behavior. These methods present some advantages over the classical ones since they do not rely on the precise knowledge of mathematical models and parameters. The paper proposes high performance speed and current control of synchronous reluctance motor(SynRM) drive using adaptive learning mechanism-fuzzy neural network (ALM-FNN) and fuzzy logic control (FLC) controller. The proposed controller is developed to ensure accurate speed and current control of SynRM drive under system disturbances and estimation of speed using artificial neural network(ANN) controller. Also, this paper proposes the analysis results to verify the effectiveness of the ALM-FNN, FLC and ANN controller.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2009.05a
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• pp.416-419
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• 2009

The widely used control theory based design of PI family controllers fails to perform satisfactorily under-parameter variation, nonlinear or load disturbance. In high performance applications, it is useful to automatically extract the complex relation that represent the drive behaviour. The use of loaming through example algorithms can be a powerful tool for automatic modelling variable speed drives. They can automatically extract a functional relationship representative of the drive behavior. These methods present some advantages over the classical ones since they do not rely on the precise knowledge of mathematical models and parameters. The paper proposes high performance speed and current control of synchronous reluctance motor(SynRM) drive using adaptive loaming mechanism-fuzzy neural network (ALM-FNN) and fuzzy logic control(FLC) controller. The proposed controller is developed to ensure accurate speed and current control of SynRM drive under system disturbances and estimation of speed using artificial neural network(ANN) controller. Also, this paper proposes the analysis results to verify the effectiveness of the ALM-FNN and ANN controller.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.55 no.2
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• pp.98-106
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• 2006

This paper proposes to position and speed control of interior Permanent magnet synchronous motor(IPMSM) drive without mechanical sensor. Also, this paper develops a adaptive fuzzy controller based fuzzy logic control for high performance of PMSM drives. In the proposed system, fuzzy control is used to implement the direct controller as well as the adaptation mechanism. A Gopinath observer is used for the mechanical state estimation of the motor. The observer was developed based on nonlinear model of IPMSM, that employs a d-q rotating reference frame attached to the rotor. A Gopinath observer is implemented to compute the speed and position feedback signal. The validity of the proposed scheme is confirmed by various response characteristics.

• Proceedings of the KIEE Conference
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• 2001.04a
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• pp.329-331
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• 2001

This paper representes a realization of a fuzzy logic speed controller for BLDC motor drives. Fuzzy sets are regulated by using parameters of BLDC motor. Simplified reasoning methods are used for defuzzification. Fuzzy logic speed controller is designed by using the high performance of DSPchip (TMS320F240). By experiment, it is confirmed that the speed of BLDC motor well follows an command speed in the load variables or low-speed area.