• Title/Summary/Keyword: 자기부상 RGV

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Design of PID Controller for Magnetic Levitation RGV Using Genetic Algorithm Based on Clonal Selection (클론선택기반 유전자 알고리즘을 이용한 자기부상 RGV의 PID 제어기 설계)

  • Cho, Jae-Hoon;Kim, Yong-Tae
    • Journal of the Korean Institute of Intelligent Systems
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    • v.22 no.2
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    • pp.239-245
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    • 2012
  • This paper proposes a novel optimum design method for the PID controller of magnetic levitation-based Rail-Guided Vehicle(RGV) by a genetic algorithm using clone selection method and a new performance index function with performances of both time and frequency domain. Generally, since an attraction type levitation system is intrinsically unstable and requires a delicate controller that is designed considering overshoot and settling time, it is difficult to completely satisfy the desired performance through the methods designed by conventional performance indexes. In the paper, the conventional performance indexes are analyzed and then a new performance index for Maglev-based RGV is proposed. Also, an advanced genetic algorithm which is designed using clonal selection algorithm for performance improvement is proposed. To verify the proposed algorithm and the performance index, we compare the proposed method with a simple genetic algorithm and particle swarm optimization. The simulation results show that the proposed method is more effective than conventional optimization methods.

Design of Levitation Controller with Optimal Fuzzy PID Controller for Magnetic Levitation System (최적 퍼지PID제어기를 이용한 자기부상시스템의 부상제어기 설계)

  • Cho, Jae-Hoon;Kim, Yong-Tae
    • 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.