• Proceedings of the KIEE Conference
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• 2002.07b
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• pp.1137-1139
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• 2002

In this paper, recurrent artificial neural network (RNN) based self tuning speed controller is proposed for the high performance drives of induction motor. RNN provides a nonlinear modeling of motor drive system and could give the information of the load variation, system noise and parameter variation of induction motor to the controller through the on-line estimated weights of corresponding RNN. Thus, proposed self tuning controller can change gains of the controller according to system conditions. The gain is composed with the weights of RNN. For the on-line estimation of the weights of RNN, extended kalman filter (EKF) algorithm is used. Self tuning controller that is adequate for the speed control of induction motor is designed. The availability of the proposed controller is verified through the MATLAB simulation with the comparison of conventional PI controller.

• Journal of Advanced Marine Engineering and Technology
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• v.27 no.3
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• pp.388-394
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• 2003

Hydraulic system is difficult to obtain a suitable performance due to the nonlinearity load pressure change and system parameter variation. The requirement of control a1gorithm has been complex in order to satisfy the performance. The adaptive control is a control method which is suggested to achieve the control object under the plant characteristics change. In spite of the case that plant characteristics and the degree of variation are difficult to grasp. the adaptive control could keep the characteristics of closed-loop system generally. In this study. a method of combined generalized minimum variance adaptive control (GMVAC) and output error feedback is proposed, in order to solve the problem of non-minimum phase of plant and the vibration and overshoot in initial response. The control performance according to the variation of characteristics of plant is evaluated by changing the supply pressure. The experimental results show the effectiveness of the proposed scheme.

• Proceedings of the KSR Conference
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• 2009.05a
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• pp.940-945
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• 2009

This paper presents the design method of low order controller for the active pantograph of electric train system. The pantograph is the most playa role to supply constant current to the train. The design objectives are to have good tracking performance about reference contact force despite the stiffness variation that is like sinusoidal function concerned in train speed or span length of contact wire. In this paper, we consider stiffness variation from external disturbance of active pantograph to simplify model equation, and propose simple second-order controller which is designed by Characteristic ratio assignment(CRA) control method. Finally, we verify time response appling to model equation of real system and frequency response about parameter uncertainty like stiffness variation. it is performed by Matlab version 6.5 and Matlab simulink simulation.

• Proceedings of the KIPE Conference
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• 2001.12a
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• pp.7-10
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• 2001

To obtain a high performance in a direct vector controlled induction machine, it is essential to correct estimation of rotor flux. The accuracy of flux observers for induction machines inherently depends on parameter sensitivity. This paper presents an analysis method for conventional flux observers using Parameter Sensitivity. The Parameter sensitivity is defined as the ratio of the percentage change in the system transfer function to the percentage change of the parameter variation. We define the ratio between real flux and estimated flux as the transfer function, and analyzed a parameter sensitivity of this transfer function.

• Proceedings of the KIEE Conference
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• 2001.11b
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• pp.92-95
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• 2001

This study presents neural load disturbance observer that used to deadbeat load torque observer and regulation of the compensation gain by parameter estimator. As a result, the response of PMSM fellows that of the nominal plant. The load torque compensation method is compose of a neural deadbeat observer. To reduce of the noise effect, the post-filter, which is implemented by MA process, is proposed. The parameter compensator with RLSM(recursive least square method) parameter estimator is suggested to increase the performance of the load torque observer and main controller. The proposed estimator is combined with a high performance neural torque observer to resolve the problems. As a result, the proposed control system becomes a robust and precise system against the load torque and the parameter variation. A stability and usefulness, through the verified computer simulation, are shown in this paper.

• Structural Engineering and Mechanics
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• v.53 no.6
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• pp.1127-1141
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• 2015

The optimum design of liquid column dampers in seismic vibration control considering system parameter uncertainty is usually performed by minimizing the unconditional response of a structure without any consideration to the variation of damper performance due to uncertainty. However, the system so designed may be sensitive to the variations of input system parameters due to uncertainty. The present study is concerned with robust design optimization (RDO) of liquid column vibration absorber (LCVA) considering random system parameters characterizing the primary structure and ground motion model. The RDO is obtained by minimizing the weighted sum of the mean value of the root mean square displacement of the primary structure as well as its standard deviation. A numerical study elucidates the importance of the RDO procedure for design of LCVA system by comparing the RDO results with the results obtained by the conventional stochastic structural optimization procedure and the unconditional response based optimization.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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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.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.13 no.3
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• pp.34-42
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• 2005

The objectives of the study are to present simple physical and mathematical models of liquid fuel in the tank of an aerospace vehicle such launch vehicle or missile and to investigate its dynamic stability for a parameter space. In this paper, liquid in the container is modeled as multi-mass system subject to parametric excitations, and a stability diagram for determination of stable-unstable regions of the motion is obtained by using an analytical method. Also, computer simulations are conducted at various parameter points to verify the analytical results, and time histories of motion are compared to explain the effect of variation of parameters of the system.

• Journal of the Korean Society for Precision Engineering
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• v.9 no.3
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• pp.29-41
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• 1992

According to the recent increase of demands for multi-function and economics on hydraulic excavator, it is required that excavator should have simple operation, higher and operational efficiency, however the modeling of engine/pump system of excavator is not prescribed by the paper. So, in this paper the modeling of engine/pump system of excavator is suggested by identification method from step response and verified effectiveness of identification system by comparing with experimental results which was conducted using PID controller. To improve the problem of parameter variation and modeling error in the system, sliding mode control was introduced and new switching surface was designed. This control algorithm was applied to a hydraulic excavator by simulation, and its effectiveness was verified, and the results of variable structure system for the excavator system using a output component was compared with that of full state feedback when load disturbances and system paramenter variation exist.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.447-452
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• 1998

A fault is considered as a variation of physical parameters; therefore the design of fault detection and identification(FDI) can be reduced to the parameter identification of a non linear system and to the association of the set of the estimated parameters with the mode of faults. ANFIS(Adaptive Neuro-Fuzzy Inference System) which contains multiple linear models as consequent part is used to model non linear systems. In this paper, we proposes an FDI system for non linear systems using ANFIS. The proposed diagnositc system consists of two ANFISs which operate in two different modes (parallel-and series-parallel mode). It generates the parameter residuals associated with each modes of faults which can be further processed by additional RBF (Radial Basis function) network to identify the faults. The proposed FDI scheme has been tested by simultation on a two-tank system