• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.704-707
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• 1997

In this paper, we present a fuzzy logic based tuner for continuous on-line tuning of PID controllers. The essential idea of the scheme is to parameterize a Ziegler-Nichols-like tuning formula by a singler parameter .alpha., then to use an on line fuzzy logic to self-tune the parameter. The adaptive scaling makes the controller robust against large variations in parametric and dynamics uncertainties in the plant model. New self-tuning controller has the ability to decide when to use PI or PID control by extracting process dynamics from relay experiments. These scheme lead to improved performance of the transient and steady state behavior of the closed loop system, including processes with nonminimum phase processes.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 제36회 하계학술대회 논문집 D
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• pp.2525-2527
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• 2005

큰 지연시간을 갖는 고차계 시스템에 대하여 일반적으로 적용할 수 있는 PID 제어기의 동조방법중 한 가지 방법으로써 축소모델을 이용하는 방법이 있다. 이 방법은 큰 지연시간을 갖는 고차계 공정을 SOPTD(Second Order Plus Time Delay Model)로 축소를 하여 SOPTD의 고정된 형태의 모델에 대하여 PID 제어기를 동조하는 방법이다. SOPTD로 모델을 축소하는 방법과 최적화 PID 파라미터를 동조하는 방법이 제시되었다. 본 논문에서는 기존의 최적화 PID 제어구조에 RLSE를 추가하여 실시간으로 축소모델의 계수를 보정해주는 최적화 적응형 PID 제어구조를 제안하였고, 기존의 제어구조보다 우수한 적응성을 가짐을 시뮬레이션을 통하여 보였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.2600-2605
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• 2003

The PBL (Poly-butadiene Latex) production process is a typical batch process. Changes of the reactor characteristics due to the accumulated scaling with the increase of batch cycles require adaptive tuning of the PID controller being used. In this work we propose a tuning method for PID controllers based on the closed-loop identification and the genetic algorithm (GA) and apply it to control the PBL process. An approximated process transfer function for the PBL reactor is obtained from the closed-loop data using a suitable closed-loop identification method. Tuning is performed by GA optimization in which the objective function is given by ITAE for the setpoint change. The proposed tuning method showed good control performance in actual operations.

• 한국정밀공학회지
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• 제13권10호
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• pp.94-104
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• 1996

This paper presents a new approach to the design of adaptive control system using DSPs(TMS320C31) for robotic manpulators to achieve accurate trajectory tracking by the joint angles Digital signal processors are used in implementing real time adaptive control algorithms to provide an enhanced motion control for robotic manipulators. In the proposed contorl scheme, adaptation laws are derived from the improved Lyapunov second stability analysis method based on the adaptive model reference control theory. The adaptive controller consists of an adaptive feedforward controller, feedback controller, and PID type time varying auxillary control elements. The proposed adaptive control scheme is simple in structure, fast in computation, and suitable for implementation of real-time control. Morever, this scheme does not require an accurate dynamic modeling nor values of manpipulator parameters and payload. Performance of the adaptive controller is illustated by simulation and experimental results for a SCARA robot.

• 한국지능시스템학회논문지
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• 제8권2호
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• pp.106-116
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• 1998

유연 로봇 매니퓰레이터의 위치 제어 알고리즘에 대한 연구를 하였다. 제안하는 알고리즘은 신경회로망의 학습 알고리즘에 근거한 자동 구축 퍼지 적응 제어기(ACFAC : Automaitc Constructed Fuzzy Adaptive controller)에 기본으로 한다. 제안하는 시스템은 비지도 경쟁 학습 알고리즘을 사용하여 입력 변수의 멤버십 함수와 지도 Outstar 학습 알고리즘을 사용하여 출력 정보를 학습시킨다. ACFAC는 유연 로봇 매니퓰레이터의 동력한 모델을 필요로 하지 않는다. ACFAC는 유연 로봇 매니퓰레이터의 끝점이 원하는 궤적을 따라가도록 설계되었다. 이 제어기의 입력은 위치 오차, 위치 오차의 미분 값과 오차의 variation에 의해 결정된다. ACFAC의 우수서을 보여주기 우해서 PID 제어나 신경회로망 알고리즘을 사용한 결과와 비교를 하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.2299-2301
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• 2000

In this paper, the design of PID controller using Neural networks for the control of non-linear system is presented. First, non-linear system is identified using BPN(Backpropagation Network) algorithm. This identified model is connected to the PID controller and the parameters of PID controller are updated to the direction of reducing the difference between the identified model output and model reference output in arbitrary input signal. Therefore, identified model output tracks the model reference output in an acceptable error range and the parameters of controller are updated adaptively. The output of the system has a good performance in case of both noisy and noiseless model reference and we can control the system stable in off-line when the dynamics of the system is changed.

• Structural Engineering and Mechanics
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• 제87권3호
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• pp.231-242
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• 2023

A novel combinatorial type-2 adaptive neuro-fuzzy inference system (T2-ANFIS) and robust proportional integral derivative (PID) control framework for intelligent vibration mitigation of uncertain structural system is introduced. The fuzzy logic controllers (FLCs), are designed independently of the mathematical model of the system. The type-1 FLCs, have a limited ability to reduce the effect of uncertainty, due to their fuzzy sets with a crisp degree of membership. In real applications, the consequent part of the fuzzy rules is uncertain. The type-2 FLCs, are robust to the fuzzy rules and the process parameters due to the fuzzy degree of membership functions and footprint of uncertainty (FOU). The adaptivity of the proposed method is provided with the optimum tuning of the parameters using the neural network training algorithms. In our approach, the PID control force is obtained using the generalized type-2 neuro-fuzzy in such a way that the stability and robustness of the controller are guaranteed. The robust performance and stability of the presented framework are demonstrated in a numerical study for an eleven-story seismically-excited building structure combined with an active tuned mass damper (ATMD). The results indicate that the introduced type-2 neuro-fuzzy PID control scheme is effective to attenuate plant states in the presence of the structured and unstructured uncertainties, compared to the conventional, type-1 FLC, type-2 FLC, and type-1 neuro-fuzzy PID controllers.

• 한국정보통신학회논문지
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• 제3권1호
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• pp.105-111
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• 1999

본 논문은 유전 알고리즘과 신경망을 이용하여 PID 제어기의 최적의 파라메터를 추출하는데 있다. 유전 알고리즘에 의한 제어는 off-line 동작으로서 외란이나 부하변동에 약한 면을 가지고 있다. 따라서 신경망을 제어기에 추가하여 on-line화하여 다음과 같이 개선하고자 한다. 첫째, 신경망의 순방향 동작에서 유전 알고리즘에 의해 적합한 PID 파라메터를 찾아 세대수의 증가에 따른 최적의 출력조건을 설정하고 둘째 신경망의 학습능력을 이용하여 역전파 학습에 의한 파라메터를 수정하여 외란이나 다양한 부하 변동에 대한 적응력을 시뮬레이션으로 나타낸다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1987년도 한국자동제어학술회의논문집; 한국과학기술대학, 충남; 16-17 Oct. 1987
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• pp.647-651
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• 1987

Yeast biomass in a biological continuous stirred tank reactor was controlled with an APPLE II microcomputer using adaptive control theory of bilinear systems. The controller used is as simple as a PID controller, but required less information. Cell concentration was well controlled by adjusting the inlet flow rate following the algorithm.

• International Journal of Aeronautical and Space Sciences
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• 제6권2호
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• pp.1-16
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• 2005

Adaptive control methods are studied for the Satellite to isolate vibration in spite of the nonlinear system dynamics and parameter uncertainties of disturbance. First, a centralized control scheme is developed based on the particle swarm optimization(PSO) algorithm and feedback theory to automatically tune controller gains. A simulation study of a 3 degree-of-freedom device was conducted to evaluate the performance of the proposed control scheme. Next, since a centralized control scheme is hard to construct model dynamics and not goad at performance when controller and systems environment are easily changed, a decentralized control scheme is presented to avoid these defects of the centralized control scheme from the point of view of production and maintenance. It is based on the adaptive control methodologies to find PID controller parameters. Experiment studies were conducted to apply the adaptive control scheme and evaluate the performance of the proposed control scheme with those of the conventional control schemes.