• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.973-975
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• 1998

For a nonlinear feedback linearization-full order observer/sliding mode controller (NFL-FOO/SMC), the separation principle is derived, and the closed-loop stability is proved by a Lyapunov function candidate using an addition form of the sliding surface vector and the estimation error.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.985-987
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• 1998

This paper presents a stability proof for the nonlinear feedback linearization-full order observer-based sliding mode controller (NFL-FOO-based SMC). The closed-loop stability is proved by a Lyapunov function candidate using an addition form of the sliding surface vector and the estimation error.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.976-978
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• 1998

This paper presents the stability proof of a nonlinear feedback linearization-reduced order observer/sliding mode controller (NFL-ROO/SMC). The closed-loop stability is proved by a Lyapunov function candidate using an addition form of the sliding surface vector and the estimation error.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.979-981
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• 1998

This paper presents a stability proof for the nonlinear feedback linearization-observer/sliding mode model following controller (NFL-O/SMMFC). The separation principle is derived, and the closed-loop stability is proved by a Lyapunov function candidate using an addition form of the sliding surface vector and the estimation error.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.988-990
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• 1998

This paper presents the stability proof of a nonlinear feedback linearization-reduced order observer-based sliding mode controller (NFL-ROO-based SMC). The closed-loop stability is proved by a Lyapunov function candidate using an addition form of the sliding surface vector and the estimation error.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.991-993
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• 1998

This paper presents a stability proof for the nonlinear feedback linearization-observer-based sliding mode model following controller (NFL-O-based SMMFC). The closed-loop stability is proved by a Lyapunov function candidate using an addition form of the sliding surface vector and the estimation error.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.10
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• pp.109-117
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• 1999

In this paper, a direct adaptive control, based on Lyapunov Function Candidate, is applied to a nonlinear Gunner's Primary Stabilized Head Mirror system to derive a parameter adaptation scheme; furthemore, a nonlinear sliding mode control, but also compensating the error in identification of the parameters which are even varying of have uncertain values. The performance of the adaptive controller is determined by the tracking ability to a desired model under some disturbances and the slowly varying parameters of the system. Both adaptive scheme and sliding mode play an important fole in the improvement of the nonlinear system control.

• KIEE International Transaction on Systems and Control
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• v.2D no.2
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• pp.97-107
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• 2002

In this paper, we propose the concept of orthonormalized compressed measurement for the stability analysis of discrete linear time-varying Kalman filters. Unlike previous studies that deal with the homogeneous portion of Kalman filters, the proposed Lyapunov method directly deals with the stochastically-driven system. The orthonorrmalized compressed measurement provides information on the a priori state estimate of the Kalman filter at the k-th step that is propagated from the a posteriori state estimate at the previous block of time. Since the complex multiple-step propagations of a candidate Lyapunov function with process and measurement noises can be simplified to a one-step Lyapunov propagation by the orthonormalized compressed measurement, a stochastic radius of attraction can be derived that would be impractically difficult to obtain by the conventional multiple-step Lyapunov method.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2474-2479
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• 2005

The control objectives in this paper are to move the gantry of a container crane to its target position and to suppress the transverse vibration of the payload. The crane system is modeled as an axially moving nonlinear string equation, in which control inputs are applied at both ends, through the gantry and the payload. The dynamics of the moving string are derived using Hamilton's principle. The Lyapunov function method is used in deriving a boundary control law, in which the Lyapunov function candidate is introduced from the total mechanical energy of the system. The performance of the proposed control law is compared with other two control algorithms available in the literature. Experimental results are given.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.11a
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• pp.387-392
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• 2004

The control objectives in this paper are to move the gantry of a container crane to its target position and to suppress the transverse vibration of the payload. The crane system is modeled as an axially moving string equation, in which control inputs are applied at both ends, through the gantry and the payload. The dynamics of the moving string are derived using Hamilton's principle for systems with changing mass. The Lyapunov function method is used in deriving a boundary control law, in which the Lyapunov function candidate is introduced from the total mechanical energy of the system. The performance of the proposed control law is compared with other two control algorithms available in the literature. Experimental results are given.