• 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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.299-302
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• 2003

In fuzzy control systems having large volumes of fuzzy data. one of the important problems is the improvement of execution speed in the fuzzy inference and defuzzification stages. In this paper, to improve the speedup of fuzzy controllers, we use an integer line mapping algorithm to convert [0, 1] real values in the fuzzy membership functions to integer pixels. U sing this, we propose a real-time high-speed fuzzy control system and implement a fast fuzzy processor and control system using FPGAs.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1115-1119
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• 1996

The objective of this paper is to study on the idle speed control sing the fuzzy logic controller under load disturbance. The inputs of the fuzzy controller are error of rpm and rpm variation. The output of fuzzy controller is an ISC motor step. The airflow is controlled by the ISC motor movement and the idle speed is controlled by the airflow control. During the control, air to fuel ratio was checked by LAMBDA sensor. All experiments were carried in real vehicle.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1478-1481
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• 2005

As the model of most practical system cannot be obtained, the practice of typical control method is limited. Accordingly, numerous artificial intelligence control methods have been used widely. Fuzzy control and neural network control have been an important point in the developing process of the field. This paper is proposed adaptive fuzzy-neural network based on the vector controlled interior permanent magnet synchronous motor drive system. The fuzzy-neural network is first utilized for the speed control. A model reference adaptive scheme is then proposed in which the adaptation mechanism is executed using fuzzy-neural network. Also, this paper is proposed estimation of speed of interior permanent magnet synchronous motor using artificial neural network 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 back-propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the analysis results to verify the effectiveness of the new method.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.282-284
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• 2006

Generally PI controller is used to control the speed of an induction motor. It has the good performance of speed control in case of adjusting the control parameters. But it occurred the problem to change the control parameters in the change of operation condition. In order to solve this problem, Fuzzy control or Artificial neural network is introduced in the speed control of an induction motor. However, Fuzzy control have the problems as the difficulties to change the membership function and fuzzy rule and the remaining error. Also Neural network has the problem as the difficulties to analyze the behavior of inner part. Therefore, the study on the combination of two controller is proceeded. In this paper, Speed controller of an induction motor based fuzzy-neural network is proposed and the speed control of an induction motor is performed using the proposed controller. Through the experiment, the fast response and good stability of the proposed speed controller is proved.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.3
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• pp.295-300
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• 2018

Conventionally, a PI control algorithm has been widely used as a speed control algorithm for BLDC motor. The PI control algorithm has a disadvantage in that is slow to reach the steady state due to the slow speed and torque response with various speed changes. Therefore, in this paper, PWM fuzzy logic control algorithm which can reach the steady state quickly by improving the response speed although there is a little overshoot is proposed. PWM reduces response speed and fuzzy logic control algorithm minimizes overshoot. The proposed PWM fuzzy logic control algorithm consists of DC chopper, PWM duty cycle regulator, and fuzzy logic controller. The performance and validity of the proposed algorithm is verified by simulation with Simulink of Matlab 2018a.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.3
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• pp.223-231
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• 2006

In high-speed fuzzy control systems applied to intelligent systems such as robot control, one of the most important problems is the improvement of the execution speed of the fuzzy inference. In particular, it is more important to have high-speed operations in the consequent part and the defuzzification stage. To improve the speedup of fuzzy controllers for intelligent systems, this paper presents an integer line mapping algorithm to convert [0, 1] real values of the fuzzy membership functions in the consequent part to a $400{\times}30$ grid of integer values. In addition, this paper presents a method of eliminating the unnecessary operations of the zero items in the defuzzification stage. With this representation, a center of gravity method can be implemented with only integer additions and one integer division. The proposed system is analyzed in the air conditioner control system for execution speed and COG, and applied to the truck backer-upper control system. The proposed system shows a significant increase in speed as compared with conventional methods with minimal error; simulations indicate a speedup of an order of magnitude. This system can be applied to real-time high-speed intelligent systems such as robot arm control.

• Journal of Power Electronics
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• v.12 no.2
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• pp.305-316
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• 2012

The paper presents an accurate loss model based controller of an induction motor to calculate the optimal air gap flux. The model includes copper losses, iron losses, harmonic losses, friction and windage losses, and stray losses. These losses are represented as a function of the air gap flux. By using the calculated optimal air gap flux compared with rated flux for speed sensorless indirect vector controlled induction motor, an improvement in motor efficiency is achieved. The motor speed performance is improved using a fuzzy logic speed controller instead of a PI controller. The fuzzy logic speed controller was simulated using the fuzzy control interface block of MATLAB/SIMULINK program. The control algorithm is experimentally tested within a PC under RTAI-Linux. The simulation and experimental results show the improvement in motor efficiency and speed performance.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.480-483
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• 2003

This paper deals with speed control of Sensorless DC servo motor using a FTHGO(FuzEy-Tuning High Gain observer). In this paper, we improved the problem from row speed section, the problem of sensor for detecting speed of motor, using FTHGO(Fuzzy-Tuning High-Gain Observer) with fuzzy control technique which is a class of adaptive control technique. In order to verify the performance of the FTHGO(Fuzzy-Tuning High Gain Observer) which is proposed in this paper, it is proved from the experiment to compare the case with a speed sensor to the case with FTHGO(Fuzzy-Tuning High Gain observer) in the speed control of DC servo motor.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.524-527
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• 2003

This paper proposes speed control system using a Fuzzy Logic Controller(FLC) in order to realize the speed control of Permanent Magnet AC Motor with no sensor. FLC based MRAS(Model Reference Adaptive System) estimates the speed of Permanent Magnet AC Motor. Using the estimated speed, speed control is performed. The experiment is executed to verify the propriety and the effectiveness of the proposed system.