• Transactions on Control, Automation and Systems Engineering
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• 제3권3호
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• pp.159-163
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• 2001

An input constrained receding horizon predictive control algorithm for uncertain systems with nonzero set points is proposed. for constant nonzero set points, models with uncertainty can be converted into an augmented incremental system through the use of integrators and the problem is transformed into a zero-state regulation problem for the incremental system. But the original constraints on inputs are converted into constraints on the sum of control inputs at each time instants, which have not been dealt in earlier constrained robust receding horizon control problems. Recursive state bounding technique and worst case minimizing strategy developed in earlier works are applied to the augmented incremental system to yield an offset error free controller. The resulting algorithm is formulated so that it can be solved using LP.

• 대한가정학회지
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• 제48권10호
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• pp.105-119
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• 2010

This study investigated the association between the expenditures for childrearing and the intention to have the second childbirth applying the recursive equation models. The major results were as follows. First, more than half of the households with one child did not have an intention to have the second childbirth. Second, about 40% of the household expenditure was spent for childrearing. About 36% of the childrearing expenditure was spent on the childcare and education, and about 64% on purchase of goods and services for child. Third, the variables which had a significant effect on the intention to have the second childbirth were child's age, mother's education, father's income, the private educational expenditure, and consumption expenditure for child. The intention to have the second childbirth did not have a significant effect on the level of childrearing expenditure. The implications for the family policies were suggested.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1988년도 한국자동제어학술회의논문집(국내학술편); 한국전력공사연수원, 서울; 21-22 Oct. 1988
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• pp.41-44
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• 1988

This paper represents the study of an effective self-tuning PID control for a robot manipulator to track a reference trajectory in spite of the presence of nonlinearities and parameters uncertainties in robot dynamic models. In this control scheme, an error model of the manipulator is established, for the first time, by difference between joint reference trajectory and tracked trajectory. It's model Parameters are estimated by the recursive least-square identification algorithm, and classical controller parameters are determined by pole placement method. A computer simulation study was conducted to demonstrate performance of the proposed self-tuning PID control in joint-based coordinates for a robot with payload.

• 한국공작기계학회논문집
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• 제13권1호
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• pp.28-34
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• 2004

In this study, new way of evaluating manufacturing cost Is organized and applied. In real manufacturing circumstances, tolerances of parts and assemblies are closely related to the cost. Several researches have been tried to identify the relations and set models. Moreover tolerances have influences on the maintenance of the manufacturing facilities. However Past researches have not considered the processing cost for the failed products. Therefore maintenance costs are represented as stochastic expressions, which include reliability of assembly and facilities. The stochastic nature of the maintenance cost is modeled and solved using Markov chain approach. Results show that this approach gives reliable estimations with remarkable computing time reduction.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.502-502
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• 2000

An input constrained receding horizon predictive control algorithm for uncertain systems with nonzero set points is proposed. For constant nonzero set points, models with uncertainty can be converted into an augmented incremental system through the use of integrators and the problem is transformed into a zero-state regulation problem for the incremental system. But the original constraints on inputs are converted into constraints on the sum of control inputs at each time Instants, which have not been dealt in earlier constrained robust receding horizon control problems. Recursive state bounding technique and worst case minimizing strategy developed in earlier works are applied to the augmented incremental system to yield an of set error free controller. The resulting algorithm is formulated so that it can be solved using LP.

• 대한전기학회논문지:전력기술부문A
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• 제48권4호
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• pp.410-418
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• 1999

This paper describes the use of residual and parameter identification methods for fault detection in an air handling unit. Faults can be detected by comparing expected condition with the measured faulty data using residuals. Faults can also be detected by examining unmeasurable parameter changes in a model of a controlled system using a system identification technique. In this study, AutoRegressive Moving Average with seXtrnal input(ARMAX) and AutoRegressive with eXternal input(ARX) models with both single-input/single-input and multi-input/single-input structures are examined. Model parameters are determined using the Kalman filter recursive identification method. Regression equations are calculated from normal experimental data and are used to compute expected operating variables. These approaches are tested using experimental data from a laboratory's variable-air-volume air-handling-unit.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 추계학술대회 논문집 학회본부
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• pp.398-400
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• 1996

We present the implementation of the adaptive filter for EMG signal processing using VHDL. For making ASIC, the basic FPU(floating point processor), e.g., adder, multiplier and divider, are implemented with VHDL. The FPU is simulated and the controller for the RLSL(recursive least square lattice) algorithm of the adaptive filter is implemented. Then FPU and the controller are linked and simulated. Finally the models are synthesized and the gate level is implemented.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1993년도 하계학술대회 논문집 A
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• pp.148-151
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• 1993

Load models for the analysis and simulation of power system are often introduced when the more accurate result is required. This work presents a single expressed load model as T-equivalent circuit of induction motor, for the composite characteristics of various loads. The parameters of the proposed load model are identified based on the system identification method as Recursive Least Square identification method. Case study results show the accuracy of proposed load model, and compared with some field measurements.

• Journal of Electrical Engineering and Technology
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• 제13권3호
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• pp.1147-1155
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• 2018

This paper presents an improved method to determine the internal parameters for improving accuracy of a lithium ion battery equivalent circuit model. Conventional methods for the parameter estimation directly using the curve fitting results generate the phenomenon to be incorrect due to the influence of the internal capacitive impedance. To solve this phenomenon, simple correction procedure with transient state analysis is proposed and added to the parameter estimation method. Furthermore, conventional dynamic equation for correction is enhanced with advanced RC impedance dynamic equation so that the proposed modeling results describe the battery dynamic characteristics more exactly. The improved accuracy of the battery model by the proposed modeling method is verified by single cell experiments compared to the other type of models.

• Communications for Statistical Applications and Methods
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• 제25권1호
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• pp.29-42
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• 2018

We combine the integer-valued GARCH(1, 1) model with a generalized regime-switching model to propose a dynamic count time series model. Our model adopts Markov-chains with time-varying dependent transition probabilities to model dynamic count time series called the generalized regime-switching integer-valued GARCH(1, 1) (GRS-INGARCH(1, 1)) models. We derive a recursive formula of the conditional probability of the regime in the Markov-chain given the past information, in terms of transition probabilities of the Markov-chain and the Poisson parameters of the INGARCH(1, 1) process. In addition, we also study the forecasting of the Poisson parameter as well as the cumulative impulse response function of the model, which is a measure for the persistence of volatility. A Monte-Carlo simulation is conducted to see the performances of volatility forecasting and behaviors of cumulative impulse response coefficients as well as conditional maximum likelihood estimation; consequently, a real data application is given.