• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.45.1-45
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• 2001

This paper proposes a Mamdani fuzzy PID controller for controlling a process with small dead time. The controller composes of a parallel structure of fuzzy PI controller and fuzzy PD controller. Each controller has two inputs, error and change of error. Hence, the control signal of the proposed controller is the average value of the output of the fuzzy PI and PD controllers. The Mamdani fuzzy PID controller is easily to be adjusted to meet the desired control system performances both in transient state and steady state. The simulation results of the proposed Mamdani fuzzy PID controller by using the same parameters (proportional gain, integral time and derivative time) as the conventional PID controller are shown. The response of the Mamdani fuzzy PID control system is faster than the conventional PID control system. Both system responses have ...

• 대한전기학회논문지:시스템및제어부문D
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• 제49권2호
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• pp.55-61
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• 2000

In this paper, we proposed a new PID tuning algorithm by the fuzzy set theory to improve the performance of the PID controller. The new tuning algorithm for the PID controller has the initial value of parameter Kc, $\tau$I, $\tau$D by the Ziegler-Nichols formula using the ultimate gain and ultimate period from a relay tuning experiment. We get error and error change of plant output correspond to the initial value and new proportion gain(Kc) and integral time($\tau$I) from fuzzy tunner. This fuzzy tuning algorithm for PID controller considerably reduced overshoot and rise time compare to any other PID controller tuning algorithms. In real parametric uncertainty systems, the PID controller with Fuzzy auto-tuning give appreciable improvement in the performance. The significant properties of this algorithm is shown by simulation In this paper, we proposed a new PID algorithm by the fuzzy set theory to improve the performance of the PID controller.

• Journal of Electrical Engineering and Technology
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• 제13권2호
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• pp.969-980
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• 2018

This paper aims to design of the neutrosophic fuzzy-PID controller and it has been compared with the conventional fuzzy-PID controller for position tracking control in terms of robustness. In the neutrosophic fuzzy-PID controller, error (e) and change of error (ce) were assessed separately on two fuzzy inference systems (FISs). In this study, the designed method is different from the conventional fuzzy logic controller design, membership degrees of antecedent variables were determined by using the T(true), I(indeterminacy), and F(false) membership functions. These membership functions are grouped on the universe of discourse with the neutrosophic set approach. These methods were tested on three-dimensional (3-D) position-tracking control application of a spherical robot manipulator in the MATLAB Simulink. In all tests, reference trajectory was defined for movements of all axes of the robot manipulator. According to the results of the study, when the moment of inertia of the rotor is changed, less overshoot ratio and less oscillation are obtained in the neutrosophic fuzzy-PID controller. Thus, our suggested method is seen to be more robust than the fuzzy-PID controllers.

• 중소기업융합학회논문지
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• 제4권1호
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• pp.31-39
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• 2014

PID 오토 튜닝 컨트롤러는 퍼지 논리를 통해 설계되었다. 이러한 오류 및 오류 파생 의견으로 일반적인 값은 발견적 표현으로 변경, 그들은 퍼지 및 defuzzification 과정을 통해 PID 이득을 결정했다. 퍼지 절차 및 PID 제어기 설계는 개별적으로 간주하고, 그것들을 혼합하고, 분석 하였다. 퍼지 논리에 의해 획득 자동 조정 PID 컨트롤러는 3 차 플랜트 제어 이하의 능력을 보여 주었다. 또한 설계된 자동 동조 방식으로 추적 문제를 참조하는 데 적용한다.

• 한국정보통신학회논문지
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• 제3권2호
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• pp.403-410
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• 1999

PID 제어기는 미지의 Deadzone을 가진 시스템에 적용될 때, 제어 성능이 저하되는 단점을 나타낼 수 있다. 이러한 문제를 해결하기 위하여, 본 논문에서는 2층 구조의 퍼지 논리를 이용한 PID 제어기를 제안하였다. 이 제어기의 구조는 전형적인 PID 제어기에 퍼지 보상기와 퍼지 동조기를 추가하여 구성하였다. 제안된 제어 방법은 전형적인 PID 제어기에 비하여 뛰어난 과도응답과 정상상태 성능을 나타낸다. 이 방법이 플랜트의 정상상태 이득뿐만 아니라 Deadzone 비선형성에서도 강인하다. 제안된 제어기의 성능은 시뮬레이션을 통하여 검증하였다

• 전기학회논문지
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• 제66권4호
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• pp.701-708
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• 2017

This paper proposes an optimum design method using Teaching-Learning-based optimization for the fuzzy PID controller of Magnetic levitation rail-guided vehicle. Since an attraction-type levitation system is intrinsically unstable, it is difficult to completely satisfy the desired performance through the conventional control methods. In the paper, a fuzzy PID controller with fixed parameters is applied and then the optimum parameters of fuzzy PID controller are selected by Teaching-Learning optimization. For the fitness function of Teaching-Learning optimization, the performance index of PID controller is used. To verify the performances of the proposed method, we use a Maglev model and compare the proposed method with the performance of PID controller. The simulation results show that the proposed method is more effective than conventional PID controller.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.86-86
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• 2000

This paper describes a neural network based fuzzy type PID control scheme. The PID controller is being widely used in industrial applications. however, it is difficult to determine the appropriate PID gains for (he nonlinear system control. In this paper, we re-analyzed the fuzzy controller as conventional PID controller structure, and proposed a neural network based fuzzy type PID controller whose scaling factors were adjusted automatically. The value of initial scaling factors of the proposed controller were determined on the basis of the conventional PID controller parameters tuning methods and then they were adjusted by using neural network control techniques. Proposed controller was simple in structure and computational burden was small so that on-line adaptation was easy to apply to. The result of practical experiment on the magnetic levitation system, which is known to be hard nonlinear, showed the proposed controller's excellent performance.

• 대한전기학회논문지:시스템및제어부문D
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• 제50권1호
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• pp.22-30
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• 2001

This paper describes a neural network based fuzzy PID control scheme. The PID controller is being widely used in industrial applications. However, it is difficult to determine the appropriated PID gains in nonlinear systems and systems with long time delay and so on. In this paper, we re-analyzed the fuzzy controller as conventional PID controller structure, and proposed a neural network based self tuning fuzzy PID controller of which output gains were adjusted automatically. The tuning parameters of the proposed controller were determined on the basis of the conventional PID controller parameters tuning methods. Then they were adjusted by using proposed neural network learning algorithm. Proposed controller was simple in structure and computational burden was small so that on-line adaptation was easy to apply to. The experiment on the magnetic levitation system, which is known to be heavily nonlinear, showed the proposed controller's excellent performance.

• 한국항행학회논문지
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• 제20권3호
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• pp.226-231
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• 2016

이동로봇의 제어는 로봇 분야에 있어 중요한 이슈이다. 이동로봇의 자율주행은 다양한 작업 환경에서 중시되고 있다. 자율 주행을 위해 이동로봇은 장애물을 감지, 회피하며 지능시스템을 도입한 제어 방식들을 사용해 충돌회피의 성능을 보완하는 연구가 활발히 진행되고 있다. 본 논문에서는 이동 로봇의 기구학적 모델을 분석하고 조향각 제어를 위한 type-2 fuzzy self-tuning PID 제어기를 설계하였다. Type-2 fuzzy 제어기는 type-1 fuzzy 제어기와 달리 복수 개의 값을 가지므로 언어표현의 모호함의 자유도가 높다. 본 논문에서는 설계된 제어기와 기존의 PID 제어기, type-1 fuzzy self-tuning PID 제어기를 비교하기 위한 방법으로 MATLAB Simulink를 사용하여 시뮬레이션을 하였다. 시뮬레이션 비교 결과 기존의 PID제어기와 type-1 fuzzy self-tuning PID 제어기의 성능보다 type-2 fuzzy self-tuning PID 제어기의 성능이 우수하다는 것을 확인하였다.

• 전기학회논문지
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• 제61권2호
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• pp.303-311
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• 2012

In this study, we introduce a dynamic process model as well as the design methodology of optimized fuzzy controller for its efficient application to vacuum production system to produce a semiconductor, solar module and display and so on. In a vacuum control field, PID control method is widely used from the viewpoint of simple structure and preferred performance. But, PID control method is very sensitive to the change of environment of control system as well as the change of control parameters. Therefore, it's difficult to get a preferred performance results from target system which has a complicated structure and lots of nonlinear factors. To solve such problem, we propose the design methodology of an optimized fuzzy PID controller through a following series of steps. First a dynamic characteristic of the target system is analyzed through a series of experiments. Second the process model is built up and its characteristic is compared with real process. Third, the optimized fuzzy PID controller is designed using genetic algorithms. Finally, the fuzzy controller is applied to target system and then its performance is compared with that of other conventional controllers(PID, PI, and Fuzzy PI controller). The performance of the proposed fuzzy controller is evaluated in terms of auto-tuned control parameters and output responses considered by ITAE index, overshoot, rise time and steady state time.