• 유공압시스템학회논문집
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• 제5권1호
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• pp.1-5
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• 2008

Pneumatic driving systems have hard non-linear characteristic and large friction force compared with driving power. Hence, it cannot be robust against parameter uncertainties, modelling error, disturbance and noise. In this study, we apply a mixed $H_2/H_{\infty}$ control to the generalized plant for a pneumatic driving apparatus system including parameter uncertainty and disturbance. In order to design the $H_2/H_{\infty}$ controller, we use the LMI technique. To evaluate control performance and robust stability of the designed controller, we compare it with a conventional controller such as PVA(Position-Velocity-Acceleration state controller) using the simulation results. As a result, it can be known that designed controller shows better robust stability than the conventional controller.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2001년도 춘계학술대회 학술발표 논문집
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• pp.144-147
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• 2001

This paper presents adaptive fuzzy controller which is uncertainty or unknown variation in different parameters with nonlinear system of helicopter. The proposed adaptive fuzzy controller applied TSK(Takagi-Sugeno-Kang) fuzzy system which is not only low number of fuzzy rule, and a linear input-output equation with a constant term, but also can represent a large class of nonlinear system with good accuracy. The adaptive law was designed by using Lyapunov stability theory. The adaptive fuzzy controller is a model reference adaptive controller which can adjust the parameter $\theta$ so that the plant output tracks the reference model output. First of all, system of helicopter was considered as stopping state, and design of controller was simulated from dynamics equation with stopping state. Results show that it is controlled more successfully with a model reference adaptive controller than with a non-adaptive fuzzy controller when there is a modelling error between system and model or a continuous added noise in such unstable system.

• 한국수소및신에너지학회논문집
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• 제29권3호
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• pp.267-273
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• 2018

From the experiments of various temperature and gas compositions, total resistance which is composed of ohmic resistance, anode resistance, cathode resistance and Nernst loss was calculated wit simple assumption. In this work, the anode and the cathode resistance was modelled with new equation which can account for the correlation between the operating temperature and the gas composition. The proposed model can predict the resistance with maximum error of 2.57% and employed in the simulation of molten carbonate fuel cells.

• 전기학회논문지
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• 제64권8호
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• pp.1217-1223
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• 2015

In this paper, we study the problem of torque ripple minimization in Brushless DC Motors (BLDC) and proposes a disturbance observer (DOB) based controller in order to efficiently reduce the torque ripple. In the DOB based control system, an equivalent disturbance (plant disturbance and effect of modelling error) is cancelled by its estimate. When the DOB controller is applied to BLDC motors, the effect of inverter switching is considered as an equivalent disturbance and to be cancelled by the DOB controller. Through computer simulations, it is shown that the performance of the proposed DOB controller is superior to that of the conventional PI controller. In the case where the numerical values of resistance and inductance are not known exactly, it is shown that the proposed DOB controller achieves better performance than the PI controller.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 하계학술대회 논문집 B
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• pp.670-672
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• 1995

This paper deals with the robust two-degree-of-freedom multivariable control system using $H_{2}/H{\infty}$optimization method which can achieve the robust stability and the robust performance, simultaneously. The feedback controller can obtain the robust stability property. The feedforward controller can obtain the robust performance property under modelling error. The robust two-degree-of-freedom multivariable control system is applied to the nonlinear multivariable boiler-turbine system. The validity of the proposed method is verified though being compared with LQG/LTR design method.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2005년도 춘계학술대회 논문집
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• pp.103-108
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• 2005

In this paper, a controller design procedure for an electro-hydraulic positioning systems have developed using $H_\infty$ control theory. The generalized models and weighting functions for a multiplicative uncertainty modelling error is presented along with $H_\infty$ controller designs in order to investigate the robust stability and performance. The multiplicative uncertainty case is specifically suited for the design of an electro-hydraulic positioning control systems using $H_\infty$ control.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1987년도 전기.전자공학 학술대회 논문집(I)
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• pp.234-237
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• 1987

Recently various control algorithms for industrial manipulators have been proposed. However, computation time, modelling error, and torque type controller design have prevented real-time implementation. As the result, most of performance evaluations of control algorithms have been carried out only by computer simulations. In this paper, we explore real-time implementation to show the feasibility and effectiveness of such algorithms. Experimental results indicate that computed torque method and learning control algorithms can be effectively applied to control industrial manipulators.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1993년도 하계학술대회 논문집 A
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• pp.214-217
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• 1993

This paper presents a new identification method which utilizes fuzzy inference in parameter identification. The proposed system has an additional control loop where a real plant is replaced by a plant model. The control system to be designed is to satisfy the following specifications: 1) It has zero steady-state error. 2) It has adequate damping characteristics. 3) 1),2) satisfied, it has a shortest rise-time. Fuzzy rules describe the relationship between comparison results of the features and magnitude of modification in the model parameter values. This method is effective in auto-tuning because the response of the closed loop is verified. The proposed method is tested in simulation for several plants with first- order lags and dead-times. The results show that the proposed method is effective in practical use.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1993년도 하계학술대회 논문집 A
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• pp.224-226
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• 1993

Multi-layered neural networks offer an exciting alternative for modelling complex non-liner systems. This paper investigates the identification of continuous time nonliner system using neural networks with a single hidden layer. The digital low - pass filter are introduced to avoid direct approximation of system derivatives from sampled data. Using a pre-designed digital low pass filter, an approximated discrete-time estimation model is constructed easily. A continuous approximation liner model is first estimated from sampled input-out signals. Then the modeling error due to the nonlinearity is decreased by a compensator using neural network. Simulation results are given to demonstrate the effective of the proposed method.

• 터널과지하공간
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• 제6권1호
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• pp.57-63
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• 1996

The goal of this study is to develop a simulation model of the pollutant dispersion in vehicle tunnels, which can be utilized to optimize the tunnel ventilation system. Contaminant dispersion is modelled using a FDM solution of advective diffusion equation. Taking into consideration the local vehicle emission rates by year, it is user-oriented and its logic is generalized. Therefore, differences in the ventilation scheme can be easily adapted. The results of its application to a urban tunnel show that the relative errors are 1.1~6.8% for the natural velocity, 1.3% for the traffic-induced velocity and 2.9% for the total air quantity. Simulated CO concentrations along the entire tunnel show about 13% of the relative error.