• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.295-298
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• 1996

In this paper, a self-tuning optimal control algorithm is proposed to retain the optimal performance of an active suspension system, when the vehicle has some time varying parameters and parameter uncertainties. We consider a 2 DOF time-varying quarter car model which has the parameter variation of sprung mass, suspension spring constant and suspension damping constant. Instead of solving algebraic riccati equation on line, we propose a neural network approach as an alternative. The optimal feedback gains obtained from the off line computation, according to parameter variations, are used as the neural network training data. When the active suspension system is on, the parameters are identified by the recursive least square method and the trained neural network controller designer finds the proper optimal feedback gains. The simulation results are represented and discussed.

• 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.

• 전자공학회논문지SC
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• 제39권6호
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• pp.1-8
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• 2002

본 논문에서는 시간지연이 존재하고 시스템의 영점이 단위원 밖에 있으며 시스템 파라미터가 변하는 비선형 시스템에 적응하는 신경회로망을 이용한 PID구조를 갖는 일반화 최소분산 자기동조제어기를 제안한다. 신경회로망은 제어기 파라미터를 추정하며 제어 출력은 추정된 제어기 파라미터로부터 얻어진다. 제어 알고리듬의 타당성을 확인하기 위해 시간 지연이 있고 일정한 시간이 경과한 후 시스템의 파라미터가 변하는 비선형 비최소위상 시스템에 대해 컴퓨터 시뮬레이션을 하였다. 그리고 신경회로망을 이용한 직접 적응 제어기와 비교하였다.

• 전자공학회논문지SC
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• 제41권4호
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• pp.21-28
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• 2004

본 논문에서는 다변수 비선형 시스템에 적응할 수 있는 신경회로망을 이용한 직접 다변수 자기동조 제어기를 제안한다. 제어기에 적용되는 플랜트는 고차이고 잡음, 시간지연과 상호결합 항이 존재하며 파라미터가 변하는 다변수 비선형 비최소위상 시스템이다. 비선형성은 전체적인 유계라 가정하며, 시스템은 선형부분과 비선형부분으로 분리한 형태로 구성한다. 다변수 비선형 자기동조 제어기의 제어 출력은 신경회로망으로 직접 추정된 제어기 파라미터로부터 얻어진다. 제어 알고리듬의 타당성을 확인하기 위해 시간지연이 있고 일정한 시간이 경과한 후 시스템의 파라미터가 변하는 고차 다변수 비선형 비최소위상 시스템에 대해 컴퓨터 시뮬레이션을 하였다. 그리고 신경회로망을 이용한 직접 다변수 적응 제어기와 비교하였다.

• 한국산학기술학회:학술대회논문집
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• 한국산학기술학회 2009년도 추계학술발표논문집
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• pp.630-632
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• 2009

The parameters of PID controller should be readjusted whenever system character change. In spite of a rapid development of control theory, this work needs much time and effort of expert. In this paper, to resolve this defect, after the sample of parameters in the changeable limits of system character is obtained, these parametrs are used as desired values of back propagation learning algorithm, also neural network auto tuner for PID controller is proposed by determing the optimum structure of neural network. Simulation results demonstrate that auto-tuning proper to system character can work well.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.964-967
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• 1993

This paper presents an auto tuning method of fuzzy inference using Genetic Algorithm. The determination of membership functions by human experts is a difficult problem. Therefore, some auto-tuning methods have been proposed to reduce the time-consuming operations. However, the convergence of the tuning by the previous methods depends on the initial conditions of the fuzzy model. So, we proposes an auto tuning method for the fuzzy neural network by Genetic Algorithm (ATF system). This paper shows effectiveness of the ATF system by simulations.

• 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.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 V
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• pp.2807-2810
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• 2003

In this paper, we propose a fuzzy control method for improving the control performance by automatically tuning the scaling factor. The proposed method is that automatically tune the input scaling factor and the output scaling factor of fuzzy logic system through neural network. Used neural network is ADALINE (ADAptive Linear NEron) neural network with delayed input. ADALINE neural network has simple construct, superior learning capacity and small computation time. In order to verify the effectiveness of the proposed control method, we performed simulation. The results showed that the proposed control method improves considerably on the environment of the disturbance.

• 전자공학회논문지B
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• 제32B권8호
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• pp.1119-1126
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• 1995

The construction of rule-base for a nonlinear time-varying system, becomes much more complicated because of model uncertainty and parameter variations. Furthemore, FLC does not have an ability of adjusting rule- base in responding to some sudden changes of control environments. To cope with these problems, an auto-tuning method of the fuzzy rule-base is required. In this paper, the GA-based Fuzzy-Neural control system combining Fuzzy-Neural control theory with the genetic algorithm(GA), which is known to be very effective in the optimization problem, will be proposed. The tuning of the proposed system is performed by two tuning processes(the course tuning process and the fine tuning/adaptive learning process). The effectiveness of the proposed control system will be demonstrated by computer simulations using a two degree of freedom robot manipulator.

• 제어로봇시스템학회논문지
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• 제8권1호
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• pp.80-88
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• 2002

As many sensors and actuators are used in automated systems, various industrial networks are adopted for digital control system. In order to take advantages of the networking, however, the network implementation should be carefully designed to satisfy real-time requirements considering network delays. This paper presents the implementation scheme of a networked control system via Profibus-DP network. More specifically, the effect of the network delay on the control performance was evaluated on a Profibus-DP testbed, and a GA-based PID tuning algorithm is proposed to design controllers suitable for networked control systems.