• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.2
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• pp.352-359
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• 2008

This paper presents a controller implementation using model based controller design programs-System Identification Toolkit, Control Design Toolkit, Simulation module. This method is easier and simpler than conventional controller design method. To implement speed control system of DC motor, a CompactRIO, Real-Time(RT) cntroller provided by NI(National Instruments), is used as hardware equipment. Firstly transfer function of DC motor drive system, which was a control target plant, can be acquired through System Identification Toolkit by using test input signal applied to motor and output signal from motor. And designing of pole-zero compensator satisfying desired control response performance through Control Design Toolkit, designed speed control response can be tested through Simulation Module. Finally LabVIEW program is converted to real-time program and downloaded to CompactRIO real-time controller Through experimental results to real DC motor drive system, designed speed control response is compared to simulation results.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1956-1957
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• 2007

본 논문에서는 모델기반의 제어기 설계 프로그램인 National Instruments(NI)사의 System Identification Toolkit과 Control Design Toolkit, Simulation module을 사용하여 기존의 제어기 설계방식 보다 쉽고 편리하게 제어기를 설계할 수 있었다. 직류전동기의 속도 제어시스템을 구현하기 위해서 하드웨어는 NI사에서 제공하는 실시간 제어기(Real-Time Controller:RT) CompactRIO를 사용하였다. 먼저는, 테스트 입력 신호를 전동기에 인가하고 얻은 출력신호를 통해 제어대상 플랜트인 직류전동기 구동시스템의 전달함수를 구할 수 있었다. 다음으로는 원하는 제어응답성능을 갖는 극점, 영점 제어기를 설계한 후, 모의실험을 통해 속도제어응답을 확인할 수 있었고, 실시간프로그램으로 다운로드하여 실제 전동기 구동시스템의 실험을 통해서 설계된 속도제어기의 응답 결과를 모의실험과 비교하여 검증하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.3
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• pp.279-284
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• 2014

This paper proposes a optimum design method for the Fuzzy PID controller of magnetic levitation-based Rail-Guided Vehicle(RGV). Since an attraction type levitation system is intrinsically unstable, it is difficult to completely satisfy the desired performance through the methods designed by conventional controllers. In the paper, the Fuzzy PID controller with fixed parameters are applied and then the optimum parameters of fuzzy PID controller are selected by genetic algorithm. For the fitness function of genetic algorithm, the performance index of PID controller is used. To verify the performance of the proposed method, we used Matlab/simulink model of Maglev and compared the proposed method with the performance of PID controller. The simulation results show that the proposed method is more effective than conventional PID controller.