• Transactions of the Korean Society of Mechanical Engineers
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• v.16 no.9
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• pp.1722-1727
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• 1992

In this study, techniques of adaptive input-output linearizing control of a class of uncertain nonlinear system are investigated. It is shown through concepts of signal growth rates that bounded trackings yield by adaptive input-output linearizing control law using normalized estimator. The convergence characteristics are improved significantly by using the normalized estimator. Simple example is presented as illustration.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.41.3-41
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• 2001

In this paper, we present a decentralized adaptive neural net(NN) controller for the transient stability and voltage regulation of a multimachine power system. First, an adaptively input-output linearizing controller using NN is designed to eliminate the nonlinearities and interactions between generators. Then, a robust control term which bounds terminal voltage to a neighborhood of the operating point within the desired value is introduced using only local information. In addition, we consider input saturation which exists in the SCR amplifier and prove that the stability of the overall closed-loop system is maintained regardless of the input saturation. The design procedure is tested on a two machine infinite bus power system.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.1081-1084
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• 1991

A technique of indirect adaptive control based on certainty equivalence for input output linearization of nonlinear system is proven convergent by Teel. It incorporates an adaptive observer for identifying unknown system states and parameters and input-output linearizing controller for robust tracking. In this study, we show that robustness and tracking performances are improved considerably by using its normalized form of Teel's observer-based identifier. Simple examples are presented as illustration.

• Journal of the Korean Society for Precision Engineering
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• v.9 no.4
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• pp.100-108
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• 1992

In this study, the technique of adaptive control based on certainty equivalence for the input-output linearization of nonlinear system is investigated. It is shown that the upper bound of the parameter estimation error can be represented more explicitly than Teel et al's works. Another direct approach, which stows that the adaptive input-output linearing control laws using the normalized identifier yield bounded tracing, is also presented.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.10
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• pp.697-702
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• 2004

In this paper, we proposed that the problem of controlling induction motor with magnetic saturation, is studied from an input-output feedback linearization with adaptive algorithm. is considered. An adaptive input-output feedback linearizing controller is considered under the assumption of known motor parameters and unknown load torque. In order to achieve the speed regulation with the consideration of improving power efficiency, rotor angular speed and flux amplitude tracking objectives are formulated. Simulation results are provided for illustration.

• Proceedings of the KIEE Conference
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• 2002.11c
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• pp.325-328
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• 2002

In this paper, we proposed that the problem of controlling induction motor with magnetic saturation is studied from an input-output feedback linearization with adaptive algorithm. The $\pi$-model of induction motor is considered. An adaptive input-output feedback linearizing controller is considered under the assumption of known motor parameters and unknown load torque. Simulation results are provided for illustration.