• 전자공학회논문지SC
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• 제40권4호
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• pp.264-274
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• 2003

본 논문에서는 잡음과 시간지연이 존재하며 시스템 파라미터가 변하는 비선형 비최소위상 시스템에 적응하는 신경회로망이 결합된 PID구조를 갖는 일반화 최소분산 자기동조제어기를 제안한다. PID구조를 갖는 자기동조는 PID제어기처럼 구조가 간단하고 계통을 정밀하게 제어하는 자기동조 제어기의 특성을 그대로 유지할 수 있다. 일반화 최소분산 자기동조 제어기 파라미터는 비선형 시스템을 선형시스템으로 간주하고 순환최소자승법으로 추정하며 설계계수의 값은 확률근사법인 Robbins-Monro 알고리듬을 이용하여 자동조정하였다. 역전파 학습 알고리듬을 사용하는 신경회로망 제어기는 비선형 부분의 제어를 보상하기 위해 필터된 기준입력과 필터된 플랜트 출력이 같도록 제어값을 출력한다. 컴퓨터 시뮬레이션을 통해 제안한 방법이 시스템의 파라미터가 변하는 비최소위상 시스템에 잘 적응함을 보였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1988년도 한국자동제어학술회의논문집(국내학술편); 한국전력공사연수원, 서울; 21-22 Oct. 1988
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• pp.452-456
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• 1988

A PID controller must need not only good servo response but also little operation of a control valve. We suggest a direct pole-placement PID self-tuning algorithm using the structure of derivative-of-output controller and Bezout identity. This algorithm can much reduce the change of output of controller and well follow the desired trajectory.

• 전기학회논문지
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• 제57권3호
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• pp.518-526
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• 2008

The fuzzy self-tuning PID controller is a PID controller with a fuzzy logic mechanism for tuning its gains on-line. In this structure, the proportional, integral and derivative gains are tuned on-line with respect to the change of the output of system under control. This paper deals with two types of fuzzy self-tuning PID controllers, rule-based fuzzy PID controller and learning fuzzy PID controller. As a medical application of fuzzy PID controller, the proposed controllers were implemented and evaluated in a laparoscopic surgery robot system. The proposed fuzzy PID structures maintain similar performance as conventional PID controller, and enhance the position tracking performance over wide range of varying input. For precise approximation, the fuzzy PID controller was realized using the linear reasoning method, a type of product-sum-gravity method. The proposed controllers were compared with conventional PID controller without fuzzy gain tuning and was proved to have better performance in the experiment.

• 전자공학회논문지SC
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• 제44권6호
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• pp.1-10
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• 2007

본 논문에서는 비선형 다변수 시스템에 적응할 수 있는 신경회로망을 이용한 PID 구조를 갖는 직접 다변수 자기동조 제어기를 제안한다. 제어기에 적용되는 플랜트는 잡음, 시간지연과 상호결합항이 존재하며 파라미터가 변하는 비선형 다변수 시스템이다. 비선형 다변수 시스템은 선형부분과 비선형부분으로 분리한 형태로 구성되며, 선형제어기는 외부환경 변화에 적응할 수 있는 PID 제어기 특성을 가진 자기동조 PID 제어기 이다. 선형부분의 제어기 파라미터는 순환최소자승법으로 직접 추정하고 비선형 부분의 파라미터는 신경회로망으로 추정한다. 그리고 각 부분에서 추정한 파라미터를 합한 후 비선형 다변수 일반화 자기동조 제어기의 제어법칙에 적용한다. 제어 알고리듬의 타당성을 확인하기 위해 시간 지연이 있고 일정한 시간이 경과한 후 시스템의 파라미터가 변하는 비선형 다변수 시스템에 대해 컴퓨터 시뮬레이션을 하였다. 또한 기존의 신경회로망을 이용한 직접 다변수 적응 제어기에 비해 효과적이다.

• 대한전기학회논문지:전기기기및에너지변환시스템부문B
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• 제51권12호
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• pp.691-696
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• 2002

This paper presents a neural network based self-tuning fuzzy PID control scheme with variable learning rate for induction motor speed control. When induction motor is continuously used long time, its electrical and mechanical Parameters will change, which degrade the Performance of PID controller considerably. This Paper re-analyzes the fuzzy controller as conventional PID controller structure, introduces a single neuron with a back-propagation learning algorithm to tune the control parameters, and proposes a variable learning rate to improve the control performance. Proposed scheme is simple in structure and computational burden is small. The simulation using Matlab/Simulink and the experiment using dSPACE(DS1102) board are performed to verify the effectiveness of the proposed scheme.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.1004-1007
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• 1992

In this paper, we present a solution to the PID tuning problem by optimizing a GPC(General Predictive Control) criterion. The PID structure is ensured by constraning the parameters to a feasible set defined by the discrete-time Euler approximation of the ideal continuous-time PID controller. The algorithm is ectended by incorporating heuristic rules for selection of the significant design parameters. The algorithm has been successfully tested and some results are prewented.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국제학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.496-500
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• 1993

This paper presents an end-point position control of 1-link flexible robot arm by the PID self-tuning fuzzy algorithm. The governing equation is derived by the extended Hamilton's principle and based on the Bernoullie-Euler beam theory. The governing equation is solved by applying the Laplace transform and the numerical inversion method. The arm is mounted on the translational mechanism driven by a ballscrew whose rotation is controlled by dcservomotor. Tip position is controlled by the PID self-tuning fuzzy algorithm so that it follows a desired position. This paper shows the experimental and theoretical results of tip dispalcement, and also shows the good effects reducing the residual vibration of the end-point.

• 대한기계학회논문집A
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• 제30권2호
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• pp.105-111
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• 2006

This paper describes a Neural Network based PID control scheme for pneumatic NC axes. Pneumatic systems have inherent nonlinearities such as compressibility of air and nonlinear frictions present in cylinder. The conventional PID controller is limited in some applications where the affection of nonlinear factor is dominant. A self-excited oscillation method is applied to derive the dynamic design parameters of linear model. The gains of PID controller are determined using a self tuning scheme. The experiments of a trajectory tracking control using the proposed control scheme are performed and a significant reduction in tracking error is achieved by comparing with those of a PID control.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1993년도 정기총회 및 추계학술대회 논문집 학회본부
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• pp.299-303
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• 1993

There can be two methods in control systems: one is to use a linear controller, the other is to use a nonlinear controller. The PID controller and the fuzzy controller can be said to belong the linear and the nonlinear controller respectively. In this paper, a new hybrid controller which is consist of the linear PID controller of which the gain is tuned and the nonlinear self tuning fuzzy controller is proposed. In the PID controller, an algorithm which parameterizes the proportional, the intergral, and the derivative gain as a single parameter is used to improve the performance of the PID controller. In the self tuning fuzzy controller, an algorithm which changes the shape of the triangle membership function and changes the scaling factor which is multiplied to the error and the error change. The evaluation of the performance of the suggested algorithm is carried on by the simulation for the Rhino XH-2 robot manipulator with 5 links revolute joints.

• 한국지능시스템학회논문지
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• 제12권3호
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• pp.246-254
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• 2002

In this paper auto-tuning scheme of PID controller based on the reference model has been studied for a Process control system by immune algorithm. Up to this time, many sophisticated tuning algorithms have been tried in order to improve the PID controller performance under such difficult conditions. Also, a number of approaches have been proposed to implement mixed control structures that combine a PID controller with fuzzy logic. However, in the actual plant, they are manually tuned through a trial and error procedure, and the derivative action is switched off. Therefore, it is difficult to tune. Since the immune system possesses a self organizing and distributed memory, it is thus adaptive to its external environment and allows a PDP (Parallel Distributed Processing) network to complete patterns against the environmental situation. Simulation results reveal that reference model basd tuning by immune network suggested in this paper is an effective approach to search for optimal or near optimal process control.