• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2237-2239
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• 2001

Backstepping procedures for output-feedback of nonlinear systems are considered. For these systems, a sliding mode cascade observer to estimate derivatives of the output is builded. In the 1st step of the observer, the output is estimated, and the 1st order derivative of the output is estimated via the 2nd step of the observer. Also, n-th order derivative of the output is estimated in the n+1th step of the observer. Observer errors and tracking errors converge to a residual set close to zero asymptotically.

• Ocean Systems Engineering
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• v.4 no.3
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• pp.201-213
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• 2014

Wave run-up is an important issue in offshore engineering, which is tightly related to the loads on the marine structures. In this study, a series of physical experiments have been performed to investigate the wave run-up around a vertical cylinder in transitional water depth. The wave run-ups of regular waves, irregular waves and focused waves have been presented and the characteristics in frequency domain have been investigated with the FFT and wavelet transform methods. This study focuses on the nonlinear features of the wave run-up and the interaction between the wave run-up and the cylinder. The results show that the nonlinear interaction between the waves and the structures might result wave run-up components of higher frequencies. The wave run-ups of the moderate irregular waves exhibit 2nd order nonlinear characteristics. For the focused waves, the incident waves are of strong nonlinearity and the wavelet coherence analysis reveals that the wave run-up at focal moment contains combined contributions from almost all the frequency components of the focused wave sequence and the contributions of frequency components up to 4th order harmonic levels are recommended to be included.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.50 no.4
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• pp.170-178
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• 2001

Deadbeat technique has been proposed as a digital controller for an UPS inverter to achieve the fast, response to a load variation and to conserve a very low THD under a nonlinear load condition. This scheme contains a fatal drawback, sensitivity against parameter variation and calculation time delay. This paper proposes a second order deadbeat controller, which fundamentally solves the calculation time delay problem and certainly guarantees the robustness of the parameter's variation. RLP(Repetitive Load Predictor) which predicts the load current ahead of two sampling time and FVR(Fundamental Voltage Regulator) which eliminates the fundamental errors of the output voltage are also proposed for the second order deadbeat controller to apply to UPS inverter systems. These are shown theoretically and practically through simulation and experiment.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.9
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• pp.664-668
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• 2003

A new sliding mode control method based on the lower bound of control gain is presented. Although the magnitube of the proposed control input is larger than that of the conventional control input using both lower and upper bounds, the positive-negative exchanging chattering is reduced and reaching mode is shorter. Because the proposed scheme needs only the lower bound of control gain, it is applicable to the system whose upper bound of control gain is doubtful to determine such as the control gain depends on the system states. It is proved that the proposed control method guarantees the sliding condition. The analysis of differences between the conventional method and the proposed method is given. The validity of the proposed control strategy is shown through a 2nd-order nonlinear system example.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.3
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• pp.174-180
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• 1996

The friction is one of the nonlinearities to be considered in the precise position control of a system which has electromechanical components. The friction has complicated nonlinear characteristics and depends on the velocity, the position and the time. The conventional fixed friction compensator and the controller based on linear control theory may cause the steady state position error or oscillation. The plant to be controlled in this study is a positioning system with a linear brushless DC motor(LBLDCM). The system behaves like a 4th-order model including the compliance and the friction. In this study, the plant model is simplified to a 2nd-order model to reduce the computation in on- line estimation. Also, to reduce the computation time, only the friction is estimated on-line while the mass and the viscous damping coefficient are fixed to the values obtained from off-line estimation. The validity of the proposed scheme is illustrated with the computer simulation and the experiment where the friction is compensated by using the estimation.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.9
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• pp.430-438
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• 2001

In this study, we introduce the adaptive Polynomial Neuro-Fuzzy Networks(PNFN) architecture generated from the fusion of fuzzy inference system and PNN algorithm. The PNFN dwells on the ideas of fuzzy rule-based computing and neural networks. Fuzzy inference system is applied in the 1st layer of PNFN and PNN algorithm is employed in the 2nd layer or higher. From these the multilayer structure of the PNFN is constructed. In order words, in the Fuzzy Inference System(FIS) used in the nodes of the 1st layer of PNFN, either the simplified or regression polynomial inference method is utilized. And as the premise part of the rules, both triangular and Gaussian like membership function are studied. In the 2nd layer or higher, PNN based on GMDH and regression polynomial is generated in a dynamic way, unlike in the case of the popular multilayer perceptron structure. That is, the PNN is an analytic technique for identifying nonlinear relationships between system's inputs and outputs and is a flexible network structure constructed through the successive generation of layers from nodes represented in partial descriptions of I/O relatio of data. The experiment part of the study involves representative time series such as Box-Jenkins gas furnace data used across various neurofuzzy systems and a comparative analysis is included as well.

• Industrial Engineering and Management Systems
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• v.12 no.3
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• pp.244-253
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• 2013

In industries, shipping is an important issue in improving the forecasting accuracy of sales. This paper introduces a hybrid method and plural methods are compared. Focusing the equation of exponential smoothing method (ESM) that is equivalent to (1, 1) order autoregressive-moving-average (ARMA) model equation, a new method of estimating the smoothing constant in ESM had been proposed previously by us which satisfies minimum variance of forecasting error. Generally, the smoothing constant is selected arbitrarily. However, this paper utilizes the above stated theoretical solution. Firstly, we make estimation of ARMA model parameter and then estimate the smoothing constant. Thus, theoretical solution is derived in a simple way and it may be utilized in various fields. Furthermore, combining the trend removing method with this method, we aim to improve forecasting accuracy. This method is executed in the following method. Trend removing by the combination of linear and 2nd order nonlinear function and 3rd order nonlinear function is executed to the original production data of two kinds of bread. Genetic algorithm is utilized to search the optimal weight for the weighting parameters of linear and nonlinear function. For comparison, the monthly trend is removed after that. Theoretical solution of smoothing constant of ESM is calculated for both of the monthly trend removing data and the non-monthly trend removing data. Then forecasting is executed on these data. The new method shows that it is useful for the time series that has various trend characteristics and has rather strong seasonal trend. The effectiveness of this method should be examined in various cases.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2331-2334
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• 2003

The basic difference between the EKF(Extended Kalman Filter) and UKF(Unscented Kalman Filter) stems from the manner in which Gaussian random variables(GRV) are represented for propagating through system dynamics. In the EKF, the state distribution is approximated by a GRV, which is then propagated analytically through the first-order linearization of the nonlinear system. This can possibly introduce large errors in the true posterior mean and covariance of the transformed GRV, which may lead to sub-optimal performance and sometimes divergence of the filter. However, the UKF addresses this problem by using a deterministic sampling approach. The state distribution is also approximated by a GRV, but is now represented using a minimal set of carefully chosen sample points. These sample points completely capture the true mean and covariance of the GRV, and UKF captures the posterior mean and covariance accurately up to the 2nd order(Taylor series expansion) for any nonlinearity. This paper utilizes the UKF to determine spacecraft orbit when only magnetometer is available. Several catastrophic failures of spacecraft in orbit have been attributed to failures of the spacecraft mission. Recently studies on contingency-major sensor failure cases- have been performed. For mission success, contingency design or plan should be implemented in case of a major sensor failure. Therefore the algorithm presented in this paper can be used for a spacecraft without GPS or contingency design in case of GPS failure.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.2
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• pp.608-616
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• 2018

If a general nonlinear system is linearized by the successive multiplication of the 1st and 2nd order systems, then there are four types of poles in this linearized system: the pole of the 1st order system and the equal poles, two distinct real poles, and complex conjugate pair of poles of the 2nd order system. Linear Quadratic (LQ) control is a method of designing a control law that minimizes the quadratic performance index. It has the advantage of ensuring the stability of the system and the pole placement of the root of the system by weighted matrix adjustment. LQ control by the weighted matrix can move the position of the pole of the system arbitrarily, but it is difficult to set the weighting matrix by the trial and error method. This problem can be solved using the characteristic equations of the Hamiltonian system, and if the control weighting matrix is a symmetric matrix of constants, it is possible to move several poles of the system to the desired closed loop poles by applying the control law repeatedly. The paper presents a method of calculating the state weighting matrix and the control law for moving the equal poles with Jordan blocks to two real poles using the characteristic equation of the Hamiltonian system. We express this characteristic equation with a state weighting matrix by means of a trigonometric function, and we derive the relation function (${\rho},\;{\theta}$) between the equal poles and the state weighting matrix under the condition that the two real poles are the roots of the characteristic equation. Then, we obtain the moving-range of the two real poles under the condition that the state weighting matrix becomes a positive semi-finite matrix. We calculate the state weighting matrix and the control law by substituting the two real roots selected in the moving-range into the relational function. As an example, we apply the proposed method to a simple example 3rd order system.

• Korean Chemical Engineering Research
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• v.52 no.3
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• pp.395-401
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• 2014

In this work, the reaction characteristics for the non-catalytic esterification of palm fatty acid distillate were analyzed. The esterification reaction was assumed as the pseudo homogeneous $2^{nd}$ order reversible reaction and 'reaction effectiveness factor (${\eta}$)' was used to take accounts into evaporation and reaction of water and methanol, which take place simultaneously in the liquid phase. The nonlinear programming was used to derive appropriate kinetic parameters, the reaction rate constant and mass transfer coefficient, minimizing the error between experimental data and the numerical values. Based on these parameters, the apparent activation energy was calculated to be 43.98 kJ/mol.