• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.1
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• pp.101-111
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• 1993

Many processes such as machining, injection-moulding and metal-forming are usually operated by hydraulic servo-systems. The dynamic characteristics of these systems are complex and highly non-linear and are often subjected to the uncertain external disturbances associated with the processes. Consequently, the conventional approach to the controller design for these systems may not guarantee accurate tracking control performance. An effective neuro-fuzzy controller is proposed to realize an accurate hydraulic servo-system regardless of the uncertainties and the external disturbances. For this purpose, first, we develop a simplified fuzzy logic controller which have multidimensional and unsymmetric membership functions. Secondly, we develop a neural network which consists of the parameters of the fuzzy logic controller. It is show that the neural network has both learning capability and linguistic representation capability. The proposed controller was implemented on a hydraulic servo-system. Feedback error learning architecture is adopted which uses the feedback error directly without passing through the dynamics or inverse transfer function of the hydraulic servo-system to train the neuro-fuzzy controller. A series of simulations was performed for the position-tracking control of the system subjected to external disturbances. The results of simulations show that regardless of inherent non-linearities and disturbances, an accuracy tracking-control performance is obtained using the proposed neuro-fuzzy controller.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.12 no.1
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• pp.111-116
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• 1998

Robust control for DC servo motor is needed according to the highest precision of industrial automation. However, when a motor control system with PID controller has an effect of load disturbance, it is very difficult to guarantee the robustness of control system. As a compensation method solving this problem, in this paper, PID-neural network hybrid control method for motor control system is presented. The output of neural network controller is determined by error and rate of error change occurring in load disturbance. The robust control of DC servo motor using neural network controller is demonstrated by computer simula tion.a tion.

• The Transactions of the Korean Institute of Power Electronics
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• v.6 no.1
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• pp.49-56
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• 2001

This paper presents a new approach to the problem based on neural network methods. Instead of using general controllers, neural networks PID control are used to control AC servo motor. The most popular and widely used control method in servo system control loops is PID type. PID controller has the features of simple structure, stability and reliability. But it has limitations in complex system control and can not remain above virtues under the conditions of parameters uncertain and environment uncertainties. AC servo motor controller is designed for drive of the cartesian coordinate type for machine tools.

• Proceedings of the KIPE Conference
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• 2002.07a
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• pp.170-173
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• 2002

시스템이 슬라이딩모드의 존재조건을 만족하는 경우, 전동기의 상태가 전동기의 파라미터에 의존하지 않고 결정되는 절환 초평면에 수렴하기 때문에 파라미터에 불감한 응답을 나타낸다. 본 연구는 소형 유도전동기에 의한 외란 및 파라미터의 변동에 대하여 로바스트(robust)와 동시에 정확한 응답을 나타내는 교류서보 전동기 구동시스템을 실현하는 것이다. 이를 실현하기 위해서 가변구조 제어이론에 기초한 슬라이딩모드 제어를 적용하여 유도전동기 자계 슬립주파수형 벡터제어를 실현한다. 이상적인 전동기에 대한 시뮬레이션의 결과에 의해서 관성, 점성마찰계수, 부하 토오크 등 의 파라미터의 변동 및 외란에 대하여 로바스터성이 있다는 것을 알 수 있고 슬라이딩모드 제어의 도입이 교류전동기 서보제어에 유효한 것을 확인하였다.

• ICROS
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• v.5 no.2
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• pp.7-16
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• 1999

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.5
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• pp.598-603
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• 2004

An optical head actuator of an optical disk drive consists of two servo mechanisms for the focusing and the tracking to acquire data from disk. As the rotational speed of the disk grows, the utilized lag-lead-lead compensator has known to be above its ability for precisely controlling the optical head actuator. To overcome the difficulty, this paper propose a new controller design method for optical head actuator based fuzzy proportional-integral-derivative (PID) control and the genetic algorithm(GA). It employs a two-stage control structure with a fuzzy PI and a fuzzy PD control and is optimized by the GA to yield the suboptimal fuzzy PID control performance. It is shown the feasibility of the proposed method through a numerical tracking actuator simulation.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.6
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• pp.1243-1248
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• 2018

Metal Powder Metallurgy is a manufacturing technology that makes unique model parts or a certain type of product by using a hardening phenomenon when a powder of metal or metal oxide is put it into a mold and compression-molded by a press and then heated and sintered at a high temperature. Powder metallurgical press equipment is mainly used to make the parts of automobile, electronic parts and so on, and most of them are manufactured using precise servo motor. The intelligent precision servo control powder molding press system which is designed and implemented in this paper has advantages of lowering the price and maintaining the precision by using the mechanical camshaft for the upper ram part and precisely controlling the lower ram part using the high precision servo system. In addition, OPC-based monitoring and process data collection systems are designed and implemented to provide scalability that can be applied to smart manufacturing management systems that utilize Big Data in the future.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.71-74
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• 2002

본 논문에서는 유전알고리즘(GA)을 이용한 PID계수 자동조정기법에 관한 DC 서보 모터 속도 제어기 설계를 목적으로 한다. DC 서보 모터는 수많은 제어용 기계나 로봇 등의 응용분야에 사용되고 있고 이러한 분야에서 제어기 파라미터들의 선택이 사용자의 전문적인 지식을 요구하게 된다. 따라서 일반적인 공정 기술자들은 시행착오에 의해 제어기 파라미터들을 계속적으로 반복 조절해 나가야 한다. 이와 같이 동적 시스템의 변화나 외란에 대하여 파라미터 계수를 자동 조정해야 할 경우 유전알고리즘을 사용함으로써 보다 정밀하고 최적화된 파라미터 계수값을 추종함에 따라 그 성능을 높일 수 있다. 본 논문에서는 DC 모터의 동특성을 분석하여 얻은 동특성 모델링으로부터 응답 특성이 빠르고 속도 정밀도가 향상된 구동제어가 가능한 제어기를 설계하고 이를 PID제어기와 비교 평가하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.5
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• pp.51-59
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• 1997

In this paper, we perform the position control of a DC servo motor using fuzzy neural controller. We use the Fuzzy controller for the position control, because the Fuzzy controller is designed simpler than other intelligent controller, but it is difficult to design for the triangle membership function format. Therefore we solve the problem using the BP learning method of neural network. The proposed Fuzzy neural network controller has been applied to the position control of various virtual plants. And the DC servo motor position control using the fuzzy neural network controller is performed as a real time experiment.

• The Transactions of the Korean Institute of Power Electronics
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• v.9 no.6
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• pp.612-619
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• 2004

Servo motor systems with ball-screw and timing-belt are widely used in NC, robot, FA and industrial applications. However, the nonlinear friction torque and damping effect in machine elements reduce the control performance. Especially tracking errors in trajectory control and very low velocity control range are serious due to the break-away friction and Stribeck effects. In this paper, a new double speed controller is proposed for compensation of the nonlinear friction torque. The proposed double speed controller has outer speed controller and inner friction torque compensator. The proposed friction torque compensator compensates the nonlinear friction torque with actual speed and speed error information. Due to the actual information for friction torque compensator without parameters and mathematical model of motor, proposed compensator is very simple structure and the stability is very high. The proposed compensator is verified by simulation and experimental results.