• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.505-508
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• 1997

In this paper, an auto-tuning method for fuzzy controller based on the neural network is presented. The backpropagated error of neural emulator offers the path which reforms the fuzzy controller's membership functions and fuzzy rule, and used for speed control of induction motor. For the torque control method, an indirect vector control scheme with slip calculation is used because of its stable characteristics regardless of speed. Motor input current is regulated by a current controlled voltage source PWM inverter using space voltage vector technique. Also, the scheme of current control fuzzy controller is synchronous reference frame with decoupling term. DSP(TMS320C31) is used to achieve the high speed calculation of the space voltage vector PWM and to build the self-learning fuzz. control algorithm. An IPM is used to simplify hardware design.

• 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.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.570-572
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• 2005

This paper is proposed adaptive hybrid artificial intelligent(HAI) controller for high performance of induction motor drive. The design of this algorithm based on fuzzy-neural network(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 experiment prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1113-1114
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• 2011

This paper is proposed design of high performance controller of SynRM drive using FNN and NNC. Also, This paper is proposed of designing fuzzy neural network controller(FNNC) which adopts the fuzzy logic to the artificial neural network(ANN). FNNC combines the capability of fuzzy reasoning in handling uncertain information and the capability of neural network in learning from processes. This controller is controlled speed using FNNC and model reference adaptive fuzzy control(MFC), and estimation of speed using ANN. The performance of proposed controller was demonstrated through response results. The results confirm that the proposed controller is high performance and robust under the variation of load torque and parameters.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.627-630
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• 2004

The PID type controller has been widely used in industrial application due to its simply control structure, ease of design, and inexpensive cost. However, control performance of the PID type controller suffers greatly from high uncertainty and nonlinearity of the system, large disturbances and so on. This paper presents a hybrid fuzzy logic proportional plus conventional integral derivative controller (fuzzy P+ID). In comparison with a conventional PID controller, only one additional parameter has to be adjusted to tune the fuzzy P+ID controller. In this case, the stability of a system remains unchanged after the PID controller is replaced by the fuzzy P+ID controller without modifying the original controller parameters. Finally, the proposed hybrid fuzzy P+ID controller is applied to BLDC motor drive. Simulation results demonstrated that the control performance of the proposed controller is better than that of the conventional controller.

• 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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• 1995.07a
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• pp.331-333
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• 1995

A high performance motor drive system must have a good speed command tracking, a insensitivity to a parameter variation and sampling time. In this paper, a robust speed controller for an induction motor is proposed. The speed controller is fuzzy-sliding adaptive controller and its system continuously is varied. That is, only P gain act in dynamic state, I gain in steady-state. Because this system is a sort of adaptive control system, global stability analysis is used to Lyapunov function. Consequently, in this paper application of fuzzy sliding adaptive controller to induction motor controlled by vecter control is presented and the control system is digitally implemented within DSP.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.2 no.4
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• pp.88-94
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• 2003

The DC motor has the strong characteristics in the speed response, the system parameter variations and the external influence and is used as the speed controller with its good starting torque in the distributing industry. However development of the Microprocessor which is for high speed switching program can make better control system. This paper introduce to design of the high-effective DC motor controller that is using Software Bang-Bang Program of Fuzzy algorithm and to verity a PI controller and a Fuzzy controller.

• 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.