• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1726-1727
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• 2007

In this paper, a systematic guideline is introduced to design a stable adaptive fuzzy wavelet controller with sliding mode for a class of uncertain nonlinear systems. Based on the Lyapunov synthesis approach, we construct the fuzzy wavelet controller such that it can basically control and guarantee the stability of the whole control system. On the other hand, a robust controller is design to restrain or eliminate the disturbance and assure the desired output accuracy of a control system. Some experimental results for a chaotic system are provided here to demonstrate the effectiveness of the control algorithm.

• Proceedings of the KIEE Conference
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• 2001.04a
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• pp.271-273
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• 2001

This paper presents a adaptive speed controller for field oriented controlled (FOC) permanent magnet synchronous motor (PMSM) drives. However, achieving FOC requires machine specific knowledge, and tracking of drifting motor parameters in order to maintain control. The proposed controller based on Minimum Controller Syntheses (MCS) algorithm does not require exact knowledge of motor parameters. This controller structure simplifies the design and implementation of the adaptive controller requiring less effort to synthesis than a standard MRAC system. Simulation results using Matlab/Simulink show that the proposed controller has good dynamic performances and it is insensitive to parameter variations.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2138-2139
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• 2002

In this paper we presents a speed controller for separately excited DC motor using adaptive backstepping technique. The motor was modeled in two local areas the first model is a linear one when speed is under base speed, the other is nonlinear one when field weakening is used to obtain the speed well above base speed. Then linear robust state feedback controller was designed for the linear model while adaptive backstepping controller was designed for the nonlinear model. The adaptive backstepping technique takes system nonlinearity into account in the control system design stage. The proposed controller is proved to be asymptotically stable by the Lyapunov stability theory and some simulations have been carried out to test.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.2
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• pp.125-130
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• 2001

This paper presents an indirect field-oriented (IFO) induction motor position servo drives which uses the model following adaptive controller with the artificial neural network(ANN)-based rotor resistance estimator. The model reference adaptive system(MRAS)-based 2-layer ANN estimates the rotor resistance on-line and a linear model-following position controller is designed by using the estimated the rotor resistance value. At the end, a fuzzy logic system(FLS) is added to make the position controller robust to the external disturbances and the parameter variations. The simulation results show the effectiveness of the proposed method.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.7
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• pp.997-1004
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• 1990

In this paper, a design method of a robust adaptive regulator with feedfoward path based on the explicit criterion minimization is proposed. The convergence speed of parameter estimation is improved by using the stochastic Newton minimization method in the criterion minimization algorithm, and sensitivity derivatives are used in the regulator calculation for improving the robustness of the control system. Trh proposed adaptive regulator is applied to the stable minimum-phase and nonminimum-phase system, the results are shown that control performance and disturbance compensation ability of the regulator are improved. And the choosing method of input penalty is proposed.

• International Journal of Control, Automation, and Systems
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• v.5 no.3
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• pp.307-316
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• 2007

In real system application, the interacting multiple model (IMM) based algorithm operates with the following problems: it requires less computing resources as well as a good performance with respect to the various target maneuvering, it requires a robust performance with respect to the time-varying measurement noise, and further, it requires an easy design procedure in terms of its structures and parameters. To solve these problems, an adaptive fuzzy interacting multiple model (AFIMM) algorithm, which is based on the basis sub-models defined by considering the maneuvering property and the time-varying mode transition probabilities designed by using the mode probabilities as the inputs of the fuzzy decision maker whose widths are adjusted, is proposed. To verify the performance of the proposed algorithm, a radar target tracking is performed. Simulation results show that the proposed AFIMM algorithm solves all problems in the real system application of the IMM based algorithm.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.1943-1945
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• 2001

In this paper, we present an adaptive output feedback control scheme for a class of uncertain nonlinear output-feedback form with time-varying parameters to which adaptive observer backstepping technique may not be applicable directly. In observer design, with the introduction of design function, we can deal with time-varying parameters in a very effective way. By the presented scheme, estimation error can be tuned to a desired small region around the origin via the design constants. Consequently, the observer with the presented design functions and the backstepping methodology achieve a robust regulation of the output tracking error while maintaining boundedness of all the signals and states.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.1
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• pp.150-156
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• 2001

This paper proposed a speed estimator using MRAC(Model Reference Adaptive Control) for sensorless vector control. It is robust for parameter variation or disturbance and the estimated speed is used as feedback in a vector control system. Experiment is presented to confirm the theoretical analysis.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2000.11a
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• pp.193-198
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• 2000

Switched reluctance motor drives have been finding their applications in the variable speed drives due to their relatively low cost, simple and robust structure, controllability and high efficiency. Fuzzy control does not need any model of plant. It is based on plant operator experience and heuristics. Fuzzy control is basically adaptive and gives robust performance for plant parameter variation. This paper deals with the sped control of switched reluctance motor using fuzzy controller with 7-rule based fuzzy logic. The proposed fuzzy controller is superior to the control performance of the conventional PI controller. The fuzzy controller is implemented by 80C196KC, 16 bit one-chip microcontroller.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.11
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• pp.5194-5201
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• 2011

In this paper, an adaptive nonlinear friction compensation scheme for second-order nonlinear mechanical system with a partially known nonlinear dynamic friction is proposed to achieve asymptotic position and velocity tracking in the absence of disturbances and modeling errors. It is also shown that even with disturbances and modeling errors, in contrast to existing other adaptive control schemes, by proper adjustment of design parameters, reduced error bounds on position and velocity tracking can be achieved.