• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.454-457
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• 2004

In this paper, control method proposed for effective speed control of the induction motor indirect vector control. For the induction motor drive, indirect vector control scheme that controls torque current and flux current of the stator current independently so that it can have improved dynamics. Also, neuro-fuzzy algorithm employed for torque current control in order to optimal speed control The proposed neuro-fuzzy algorithm can be applied to the precise speed control of an induction motor drive system or the field of any other power systems.

• Journal of the Korean Society of Safety
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• v.17 no.4
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• pp.87-93
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• 2002

This paper is presented new fuzzy controller for high performance of induction motor drive. New fuzzy controller took out appropriate amounts of accumulated control input according to fuzzy described situations in addition to the incremental control input calculated by conventional direct fuzzy controller. The structures of the proposed controller was motivated by the problems of direct fuzzy controller. The direct controller was given generally inevitable overshoot when one tries to reduce rise time of response. The undesirable characteristics of the direct fuzzy controller are caused by integrating operation of the controller, even though the integrator itself is introduced to overcome steady state error in provide a thorough comparative insight into the behavior of induction motor drive with direct and new fuzzy speed controller. The validity of the comparative results is confirmed by the simulation results for induction motor drive system.

• The Transactions of the Korean Institute of Power Electronics
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• v.7 no.3
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• pp.215-223
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• 2002

This paper investigates the adaptive control of a fuzzy logic based speed and flux controller fur a vector controlled induction motor drive. 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 model reference adaptive control(MRAC) fuzzy controller is evaluated by simulation for various operating conditions. The validity of the Proposed MRAC fuzzy controller is confirmed by performance results for induction motor drive system.

• Proceedings of the KIEE Conference
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• 2002.11d
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• pp.224-227
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• 2002

This paper is proposed new fuzzy controller for speed control of induction motor drive. New fuzzy controller take out appropriate amounts of accumulated control input according to fuzzily described situations in addition to the incremental control input calculated by conventional direct fuzzy controller. The structures of the proposed controller is motivated by the problems of direct fuzzy controller. Proposed controller fuzzily clear out integrated quantifies according to situation. This paper attempts to provide a thorough comparative insight into the behavior of induction motor drive with direct and new fuzzy speed controller. The validity of the comparative results is confirmed by simulation results for induction motor drive system.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.51 no.3
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• pp.111-119
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• 2002

This paper proposes a speed control system based on a fuzzy logic approach, integrated with a simple and effective adaptive algorithms. And this paper attempts to provide a thorough comparative insight into the behavior of induction motor drive with PI, direct and improved fuzzy speed controller. A indirect vector controlled induction motor is simulated under varying operating condition. The validity of the comparative results is confirmed by simulation results for induction motor drive system.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.55 no.3
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• pp.117-122
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• 2006

The paper is proposed artificial neural network(ANN) sensorless control of induction motor drive with fuzzy learning control-fuzzy neural network(FLC-FNN) controller. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also this paper is proposed. speed control of induction motor using FLC-FNN and estimation of speed using ANN controller. The back Propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed so that the actual state variable will coincide with the desired one. The proposed control algorithm is applied to induction motor drive system controlled FLC-FNN and ANN controller, Also, this paper is proposed the analysis results to verify the effectiveness of the FLC-FNN and ANN controller.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1474-1477
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• 2005

This paper is proposed maximum torque per ampere of induction motor using fuzzy-neural networks controller. Operation of maximum torque per ampere is achieved when, at a given torque and speed, the slip frequency is adjusted to that so that the stator current amplitude is minimized. This paper introduces a induction motor drive system with fuzzy-neural networks controller. A neural network-based architecture is described for fuzzy logic control. The characteristic rule and their membership function of fuzzy system are represented as the processing nodes in the neural network structure. This paper is proposed the analysis as well as the simulation results to verify the effectiveness of the new method.

• Proceedings of the KIEE Conference
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• 2001.07b
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• pp.1179-1181
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• 2001

For high performance induction motor drives such as mill drives, elevator, spindle drive, NC and so on, smart speed controls is usually required, that requires a precise current control. This paper is proposes design of fuzzy controller which makes use of the output voltage of the space vector PWM inverter. Also, proposes the performance fuzzy controller for high performance vector control of induction motor drive system. The performance of a fuzzy controller is compared with that of an PI controller in an internal loop. The validity of the proposed technique is confirmed by simulation results for induction motor drive system.

• Proceedings of the KIPE Conference
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• 2005.07a
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• pp.20-22
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• 2005

In this paper, we propose fuzzy-neural network controller that combines a fuzzy control and the Neural Networks for high performance control of induction motor drive, Also, this paper is proposed control of maximum torque per ampere of induction motor. This strategy is proposed which is simple in structure and has the honest goal of minimizing the stator current magnitude for given load torque. The performance of the proposed induction motor drive with maximum torque control using fuzzy-neural network controller is verified by simulation at dynamic operation conditions.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.53 no.9
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• pp.569-575
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• 2004

This paper is proposed adaptive fuzzy-neural network(FNN) controller for high performance of induction motor drive. The design of this algorithm based on FNN 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 adaptive FNN 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.