• 융합신호처리학회논문지
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• 제4권3호
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• pp.49-54
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• 2003

본 논문에서는 PID 제어기 응답을 보상하기위해 자기구성 신경망 보상기를 추가한 제어기를 제안한다. 기존의 PID 제어기는 제어기 설계가 간단하나 계수값을 설정하는데 많은 시행착오가 필요하다. 그리고, 신경망 제어 방식은 여러 파라미터들을 설계자의 임의에 따라 결정함으로써 최적의 구조를 갖지 못하는 단점이 있다. 본 논문에서는 이러한 문제를 해결하기위해 역전파 알고리즘을 기본으로 하여 은닉계층 노드의 활성화 함수로 가우시안 포텐셜함수를 사용하는 자기구성 신경망을 사용해, PID 제어기의 출력을 보상하도록 하였다. 자기구성 신경망은 학습을 진행함에 따라 가우시안 함수의 위치와 모양, 갯수가 자동으로 조정 되도록 하였다. 자기구성 신경망 보상기를 추가한 PID 제어기의 성능을 확인하기 위해서 2차 플랜트에 적용하여 모의 실험하였으며 DSP 프로세서를 사용하여 제어기를 구현한 후 유압 서보시스템의 속도 제어에 적용하여 실험결과를 관찰하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.142-142
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• 2000

During the operation of crane system in container yard, it is necessary to control the crane trolley position so that the swing of the hanging container is minimized. Recently an automatic control system with high speed and rapid transportation is required. Therefore, we designed a controller to control the crane system with disturbances. In this paper, Ive present the neural network two degree of freedom PID controller to control the swing motion and trolley position. Then we executed the computer simulation to verify the performance of the proposed controller and compared the performance of the neural network PID controller with our proposed controller in terms of the rope swing and the precision of position control . Computer simulation results show that the proposed controller has better performances than neural network PID with disturbances.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 학술회의 논문집 정보 및 제어부문 B
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• pp.425-428
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• 2003

In this paper, the stabilization of an inverted pendulum system is studied. Here, the PID control method is adopted to make the system stable. In order to adjust the PID gains, a three-layer neural network, which is based on the back propagation method, is used. Meanwhile, the time for training the neural network depends on the initial values of PID gains and connection weights. Hence, the genetic algorithm Is considered to shorten the time to find the desired values. Simulation results show the effectiveness of the proposed approach.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 1999년도 학술대회논문집-국제 전기방전 및 플라즈마 심포지엄 Proceedings of 1999 KIIEE Annual Conference-International Symposium of Electrical Discharge and Plasma
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• pp.262-266
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• 1999

In this paper, we consider to apply of 2-DOF (Degree of Freedom) PID controller at D.C servo motor system. Many control system use I-PD, PID control system, but the position control system have difficulty in controling variable load and changing parameter. We propose neural network 2-DOF PID control system having feature for removal disturbrances and tracking function in the target value point. The back propagation algorithm of neural network used for tuning the 2-DOF parameter ($\alpha$, $\beta$, ${\gamma}$, η). We investigate the 2-DOF PID control system in the position control system and verify the effectiveness of proposal method through the result of computer simulation.

• 한국정보통신학회논문지
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• 제15권9호
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• pp.1871-1880
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• 2011

본 논문에서는 최근 편리하고 간편한 이동수단으로 각광받고 있는 Segway 형태의 이륜 역진자로봇에 대해 기존의 방법보다 더 안정적인 밸런싱과 빠른 속도제어가 가능하도록 제어기를 설계하였다. 먼저 널리 사용되는 PID 제어 구조를 이륜 역진자로봇에 적용하고, 몇 단계로 지정된 탑승자의 각 몸무게에 대해 적절한 PID 제어기 이득을 시행착오적으로 선택하여 밸런싱과 속도제어가 잘 이루어지도록 하였다. 앞에서 지정된 몸무게 이외의 임의의 몸 무게에 대한 PID 제어기 이득값을 구하기 위해 보간 개념으로 신경회로망을 사용하였으며 앞에서 시행착오적으로 구한 제어 이득값을 학습데이터로 사용하였다. 이와 같이 신경회로망을 이용하여 설계된 제어기의 성능을 확인하기 위해서 시뮬레이션 연구를 수행하였으며, 기존의 PID 제어기보다 빨리 밸런싱과 속도제어가 됨을 확인할 수 있었다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제1권1호
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• pp.95-103
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• 2001

The purpose of Introducing a combined cycle with gas turbine in power plants is to reduce losses of energy, by effectively using exhaust gases from the gas turbine to produce additional electricity or process. The efficiency of a combined power plant with the gas turbine increases, exceeding 50%, while the efficiency of traditional steam turbine plants is approximately 35% to 40%. Up to the present time, the PID controller has been used to operate this system. However, it is very difficult to achieve an optimal PID gain without any experience, since the gain of the PID controller has to be manually tuned by trial and error procedures. This paper focuses on the neural network tuning of the 2-DOF PID controller with a combined 2-DOF parameter (NN-Tuning 2-DOF PID controller), for optimal control of the Gun-san gas turbine generating plant in Seoul, Korea. In order to attain optimal control, transfer function and operating data from start-up, running, and stop procedures of the Gun-san gas turbine have been acquired and a designed controller has been applied to this system. The results of the NN-Tuning 2-DOF PID are compared with the PID controller and the conventional 2-DOF PID controller tuned by the Ziegler-Nichols method through experimentation. The experimental results of the NN-Tuning 2-DOF PID controller represent a more satisfactory response than those of the previously-mentioned two controllers.

• 로봇학회논문지
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• 제19권2호
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• pp.139-148
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• 2024

In this paper, we propose a two-degree-of-freedom snake robot head system and an I-PID (Intelligent Proportional-Integral-Derivative)-based controller utilizing RBF (Radial Basis Function) neural network and adaptive robust terms as a control strategy to reduce rotation occurring in the snake robot head. This study proposes a two-degree-of-freedom snake robot head system to avoid complex snake robot dynamics. This system has a control system independent of the snake robot. Subsequently, it utilizes an I-PID controller to implement a control system that can effectively manage rotation at the snake robot head, the robot's nonlinearity, and disturbances. To compensate for the time delay estimation errors occurring in the I-PID control system, an RBF neural network is integrated. Additionally, an adaptive robust term is designed and integrated into the control system to enhance robustness and generate control inputs responsive to signal changes. The proposed controller satisfies stability according to Lyapunov's theory. The proposed control strategy was tested using a 9-degreeof-freedom snake robot. It demonstrates the capability to reduce rotation in Lateral undulation, Rectilinear, and Sidewinding locomotion.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.1561-1564
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• 1997

In the study, simulation result was studied by connecting PID controller in series to the established Neural Networks Controller. Neural Network model is composed of two layers to evaluate tracking performance improvement. The reqular dynamic characteristics was also studied for the expected error to be minimized by using Widrow-Hoff delta rule. As a result of the study, We identified that tracking performance inprovement was developed more in case of connecting PID than Neural Network Contoller and that tracking plant parameter in 251 sample was approached rapidly case of time variable.

• 한국항공운항학회지
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• 제22권1호
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• pp.1-6
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• 2014

In this paper, controller propose to prevent compressor surge and improve the transient response of the fuel flow control system of turbojet engine. Turbojet engine controller is designed by applying Artificial Neural Network PID control algorithm and make an inference by applying Levenberg-Marquartdt Error Back Propagation Algorithm. Artificial Neural Network inference results are used as the fuel flow control inputs to prevent compressor surge and flame-out for turbojet engine for UAV. High Gain Observer is used to estimate to compressor rotation speed of turbojet engine. Using MATLAB to perform computer simulations verified the performance of the proposed controller. Response characteristics pursuant to the gain were analyzed by simulation.

• 융합신호처리학회논문지
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• 제2권1호
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• pp.88-94
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• 2001

In the position control system, the output of a controller is generally used as the input of a plant but the undesired noise is included in the output of a controller. Therefore, there is a need to use a precompensator for rejecting the undesired noise. In this paper, the expanded PID controller with a precompensator is constructed. The precompensator and PID controller are designed by a neural network with two-hidden layer and these coefficients are changed automatically to be a desired response of system when the response characteristic is changed under a condition.