• Proceedings of the KIEE Conference
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• 2001.07b
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• pp.1032-1034
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• 2001

As well known, a solar cell has an optimal operating point to be able to get the maximum power $P_{max}$. So, many $P_{max}$ tracking controllers using the line voltage of a solar cell have been popularly used. But it may vary depending on the miss match between the solar cell output and the load. In this paper, we investigate the possibilities of $P_{max}$ control using the current tracking controller and the output voltage and the output current instead of the solar cell output power. And we also examine about the optimal power converter using ZVCS step up and down chopper circuit to operate the solar cell at an optimal voltage using these variables. And then, we show some experimental results to confirm the successful operation.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.316-318
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• 1992

The proposed algorithm uses a modified adaptive law which consists of switching -modification, normalized augmented error and low-pass filtered signal of output tracking error, furthermore, the proportional term that is a product of the output tracking error and the bounded signal having an information of output tracking error is added to the conventional control law for improvement of robustness and performance of an adaptive system. For the arbitrary nth order system, mathematical analysis and computer simulation are used to demonstrate improvement of output error characteristics, guaranteeing boundedness of all signals in the overall system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.12
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• pp.2053-2056
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• 2016

In this paper, we propose an output feedback tracking control method for the wheeled mobile robots with kinematic disturbances. The kinematic disturbances should be compensated to avoid the performance degradation. Also, the unavailable velocity of the mobile robot should be estimated. These should be estimated together by designing the nonlinear observer. Based on these estimates, the output feedback controller can be designed. The stability of the mobile robot control systems using the proposed method is rigorously analyzed and the simulation results are also provided to validate the proposed method.

• 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.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.5
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• pp.1-9
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• 1996

In this paper, a parameter identification method for a discrete-time linear system using multi-layer neural network is proposed. The parameters are identified with the combination of weights and the output of neuraons of a neural network, which can be used for a linear and a nonlinear controller. An adaptive output tracking architecture is designed for the linear controller. And, the nonlinear controller. A sliding mode control law is applied to the stabilizing the nonlinear controller such that output errors can be reduced. The effectiveness of the proposed control scheme is illustrated through simulations.

• 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.

• JSTS:Journal of Semiconductor Technology and Science
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• v.14 no.6
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• pp.802-809
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• 2014

This paper presents a dual-mode bias modulator (BM) for complementary metal oxide semiconductor (CMOS) power amplifiers (PAs). The BM includes a hybrid buck converter and a normal buck converter for an envelope tracking (ET) mode for high output power and for an average power tracking (APT) mode for low output power, respectively. The dual-mode BM and CMOS PA are designed using a $0.18-{\mu}m$ CMOS process for the 1.75 GHz band. For the 16-QAM LTE signal with a peak-to-average power ratio of 7.3 dB and a bandwidth of 5 MHz, the PA with the ET mode exhibited a poweradded efficiency (PAE) of 39.2%, an EVM of 4.8%, a gain of 19.0 dB, and an adjacent channel leakage power ratio of -30 dBc at an average output power of 22 dBm, while the stand-alone PA has a PAE of 8% lower at the same condition. The PA with APT mode has a PAE of 21.3%, which is an improvement of 13.4% from that of the stand-alone PA at an output power of 13 dBm.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.96-100
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• 2003

In this paper, a DLC method is proposed for linear feedback systems to improve the tracking performance when the task of the system is repetitive. DLC can generate the desired control input directly from the previously learned control inputs corresponding to other output trajectories. It is assumed that all the desired output functions considered in this paper have some relations called proportionality and it is shown by mathematical analysis that DLC can be utilized to generate additional control efforts for the perfect tracking. To show the validity and tracking performance of the proposed method, some simulations are performed for the tracking control of a linear system with a PI controller.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.2
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• pp.76-80
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• 2005

In this paper, a Direct Learning Control (DLC) method is proposed for linear feedback systems to improve the tracking performance when the task of the control system is repetitive. DLC can generate the desired control input directly from the previously learned control inputs corresponding to other output trajectories. It is assumed that all the desired output functions given to the system have some relations called proportionality and it is shown by mathematical analysis that DLC can be utilized to genera additional control efforts for the perfect tracking. To show the validity and tracking performance of the proposed method, some simulations are performed for the tracking control of a linear system with a PI controller.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.6
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• pp.561-567
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• 2006

This paper deals with a fuzzy controller using IA(Immune Algorithm) for Trajectory Tracking of 2-DOF WMR(Wheeled Mobile Robot). The global inputs to the WMR are reference position and reference velocity, which are time variables. The global output of WMR is a current position. The tracking controller makes position error to be converged 0. In order to reduce position error, a compensation velocities on the track of trajectory is necessary. Therefore, a FIAC(Fuzzy-IA controller) is proposed to give velocity compensation in this system. Input variables of fuzzy part are position errors in every sampling time. The output values of fuzzy part are compensation velocities. IA are implemented to adjust the scaling factor of fuzzy part. The computer simulation is performed to get the result of trajectory tracking and to prove efficiency of proposed controller.