• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.684-688
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• 1996

In this paper, to overcome drawbacks of variable structure control system a self-tuning fuzzy sliding mode control algorithm using gradient descent method is proposed. The proposed method has the characteristics which are viewed in conventional VSC, e.g. insensitivity to a class of disturbance, parameter variations and uncertainties in the sliding mode. To demonstrate its performance, the proposed control algorithm is applied to a one-degree of freedom robot arm. The results show that both alleviation of chattering and performance are achieved.

• 연구논문집
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• s.27
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• pp.35-45
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• 1997

The dynamic design process for the articulated bogie of light rail vehicle(LRV) was studied to design a primary and secondary suspension elements. Suspension stiffness and damping is selected on the basis of the ride quality and suspension stroke trade-off. LRV was modeled as a 2 d.o.f linear system for the design of vertical suspension characteristics and a 4 d.o.f linear system for the design of lateral suspension characteristics. FRA's class-4-track irregularity was used for the exciting disturbance on track. The optimum value of primary and secondary suspension characteristics was determined using this design process.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.1068-1072
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• 1991

This paper presents a decentralized model reference adaptive control scheme for an interconnected continuous linear system composed of a number of single-input single-output subsystems in which outgoing interactions pass through the measurement channel and are subject to bounded external disturbance. The scheme can treat the unknown strength of interactions as well as the uncertainty of subsystems.

• Proceedings of the KSME Conference
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• 2000.04a
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• pp.517-522
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• 2000

When a real robot manipulator is mathematically modeled. uncertainties are not avoidable. The uncertainties are often nonlinear and time-varying. The uncertain factors collie from imperfect knowledge ok system parameters. payload change. friction. external disturbance. and etc. In this paper. we propose a class of robust hybrid controls of manipulators without knowing the exact stiffness and provide the stability analysis. Simulation results are provided to show the effectiveness of the algorithms.

• Journal of the Korean Statistical Society
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• v.25 no.2
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• pp.235-241
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• 1996

The ordinary least squares estimator of the disturbance variance in the linear regression model with stationary errors is shown to be asymptotically unbiased when the error process has a spectral density bounded from the above and away from zero. Such error processes cover a broad class of stationary processes, including ARMA processes.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.10
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• pp.1-11
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• 1996

In this paper, a robust receding horizon control algorithm, which can be employed for a wide class of nonlinear systems with control and state constraints, modeling errors, and disturbances, is considered. In a neighborhood of the origin, a linear feedback controlelr for the linearized system is applied. Outside this neighborhood, a receding horizon control is applied. Robust stability is proved considering the time taken to solve an optimal control problem so that the proposed algorithm can be applied as an on-line controller.

• Korean Journal of Child Studies
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• v.17 no.2
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• pp.107-119
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• 1996

This study investigated social competence, peer contacts, and emotional development in children of lower SES employed mothers. Instruments were Choi's (1992) questionnaire on social competence, Pettit's (1988) questionnaire on peer contacts, and Kang's (1986) and Kim's (1993) questionnaires on emotional development. The data was analyzed by t-test. Social class differences were found in children's social capability, leadership and anxiety, in peer contacts, and in emotional disturbance and aggression.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.37 no.4
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• pp.220-225
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• 1988

A class of adaptive controllers with integral action is proposed, which may riject the offset due to any load disturbance on the plant. Effective integral action and robust identification against the offset can be achieved via the zero-gain predictor. The system is improved, in this paper, to be of more generalized structure, and the detuning control weight which can cope with nonminimum-phase systems is tuned on-line. Discrete-time versions of the improved system are developed, which may be more flexible for the choice of the design parameters. The resulting control systems may also be shown to be robust to the unmodelled dynamics.

• Journal of Electrical Engineering and Technology
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• v.4 no.2
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• pp.292-300
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• 2009

This paper considers a robust adaptive stabilization problem for a class of uncertain nonlinear systems which include an unknown virtual control coefficient, an unknown constant parameter, and a time-varying disturbance whose bound is unknown, We propose a new estimator for an un-known virtual control coefficient and present a robust adaptive backstepping design procedure which results in a smooth state feedback control law, a new two-dimensional parameter update law, and a $C^1$ Lyapunov function which is positive definite and proper.

• International Journal of Control, Automation, and Systems
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• v.5 no.5
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• pp.501-507
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• 2007

This paper presents a new algorithm for the closed-loop $H_{\infty}$ control of a class of singularly perturbed nonlinear systems with an exogenous disturbance, using the successive Galerkin approximation (SGA). The singularly perturbed nonlinear system is decomposed into two subsystems of a slow-time scale and a fast-time scale in the spirit of the general theory of singular perturbation. Two $H_{\infty}$ control laws are obtained to each subsystem by using the SGA method. The composite control law that consists of two $H_{\infty}$ control laws of each subsystem is designed. One of the purposes of this paper is to design the closed-loop $H_{\infty}$ composite control law for the singularly perturbed nonlinear systems via the SGA method. The other is to reduce the computational complexity when the SGA method is applied to the high order systems.