• Journal of the Korean Society for Precision Engineering
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• v.17 no.3
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• pp.90-101
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• 2000

In this paper, we propose a controller for differentially steered wheeled mobile robots. The controller uses input-output linearization algorithm and artificial neural network to stabilize the dynamic model and compensate uncertainties. The proposed neural network part has 6 inputs, 1 hidden layer, 2 torque outputs and features fast online learning and good performance on structure error learning basis. Simulation results show that the proposed controller perform precisely tracking of reference path and is robust to uncertainties.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1124-1127
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• 1996

Asymmetric cylinders are usually used as an actuator of active suspensions. Since the force is influenced not only by the control but by the road roughness, force control is needed to track the desired force. But the conventional error feedback control treats the valve-cylinder dynamics at its operating point and many use the symmetric model which differ in all respects. We adopt an asymmetric cylinder model and apply a feedback linearization method for the force control to compensate both the valve nonlinearities and the effects of the road roughness.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.939-942
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• 1989

Most of systems are included nonlinear characteristics in practice. One might be faced with difficulties when problems of nonlinear systems are solved. In this paper we present a formal linearization method of nonlinear systems by using the trigonometric Fourier expansion on the state space considering easy inversion. An error bound, an application, and a compensation of this method are also investigated.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.510-512
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• 1997

Measurement errors in a temperature measurement system are mainly due to the consisting elements' accuracies and the circuit parameters' changes following the environment variations such as temperature. Further, system's non-linearity makes the measurement accuracy worse, and accordingly a linearization method should be considered to avoid this worsening. In this study, an error-correction method and a linearization method are proposed and a system utilizing these methods is realized.

• 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.