• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.95.4-95
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• 2001

In this paper, we proposed and analyze an robust adaptive control scheme for uncertain nonlinear systems using Universal function approximators. The proposed scheme completely overcomes the singularity problem which occurs in the indirect adaptive feedback linearizing control. No projection in the estimated parameters and no switching in the control input are needed. The stability of the closed-loop systems is guaranteed in the Lyapunov standpoint.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.12
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• pp.2382-2390
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• 2007

A new polynomial-based predistortion method for linearizing nonlinear power amplifier is proposed. The proposed method finds the predistortion parameter directly without the help of postdistorter whereas most existing polynomial-based predistortion methods calculate the predistortion parameter indirectly from the prostdistorter. First, a new predistortion algorithm is derived based on the assumption that the characteristic of the amplifier is modeled by piecewise linear function. Then it is modified into a proposed method which does not require any assumption or prior knowledge of the amplifier. The proposed method is derived based on the RLS (recursive least squares) algorithm. The proposed technique is simpler to implement than the existing methods and the computer simulation demonstrates that the proposed method is more robust to the initial condition and the saturation region of the amplifier.

• Proceedings of the KIEE Conference
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• 1990.11a
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• pp.10-14
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• 1990

Variable Structure System(VSS) la being extended to a new control system of ac servo machines for its merits of simple mechanism and robustness. This paper has studied about applying VSS to position control for brushless servo motor. But VSS has the chattering problem of control input. This chattering phenomenon cause acoustic noises, torque ripple and increase harmonics of the current. One of the useful way to eliminate this defect of VSS, linearlizing the switching function is discussed here. Though the conventional method of linearizing the switching function diminishes the chattering, it may degrade the robustness of the system. In this paper, new linearized switching function which shows robust performance to the parametric variation and reduces chattering simultaneously is introduced and assured by simulation.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.6
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• pp.971-981
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• 1994

In this paper, we consider feedback-linearizing control of VR (Variable Reluctance) motors which have been increasingly used in high performance direct-drive applications. We characterize all torque controllers that can make the generated torque of a VR motor linear to torque command but without torque ripple. The torque controlles maximize the range of torque commands which are admissible under the physical limitation in stator currents. The whole class of all such torque controllers is parameterized in the explicit form which contains a function to be chosen freely. This free function can be used to achieve other control objectives as well as linear dynamic characteristics. As the examples for optimal choices of the free function, we actually determine two optimal free functions, one for minimal rate of change in current commands and the other for minimal power loss due to stator resistance. To illuminate further the practical use of torque controllers proposed in this paper, we present some experimental results for the case of a commercially available VR motor.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.4
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• pp.454-459
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• 2012

A kernel function of apparent earth resistivity can be estimated using the apparent earth resistivity measured with Wenner's 4 point method. It becomes to solve a nonlinear system to estimate the kernel function of apparent earth resistivity. However it is not simple to get solution of nonlinear system with many unknown variables. This paper suggests the method of estimating kernel function by linearizing this nonlinear system. Finally, various examples of earth structure have been simulated to evaluate the proposed method in this paper.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.12 no.5
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• pp.686-693
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• 2001

In this paper, a new carrier complex power series which represents nonlinear transfer function of high power amplifier is derived. Using this transfer function, the nonlinear transfer function of predistortive circuit for linearizing the distortion effect of a HPA(High Power Amplifier) is derived and fabricated. A measured gain and $P_{1dB}$ of the fabricated HPA in IMT-2000 basestation transmitting band are 34.06 dB and 35.4 dBm. The predistortive circuit using inverse carrier complex power series is fabricated and operated with HPA. The predistortive HPA improves C/I(Carrier to Intermodulation) ratio of HPA by 17.01 dB(@Pout=25.43 dBm/tone) with 2-tone at 2.1375 GHz and 2.1425 GHz.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.177-180
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• 1996

The purpose of this paper is to design a robust adaptive control algorithm for a class of systems having continuous relay nonlinearity. This continuous relay nonlinearity can be defined as an analytic nonlinear function having unknown parameters and bounded unmodeling part. By this mathematical modeling, the whole system can be considered as a nonlinear system having unknown parameters and bounded perturbation. The control algorithm of this paper, RALC, can be constructed by robust adaptive law, feedback linearization, and indirect robust adaptive control. By this RALC, we can obtain that the output of given system can follow that of a stable reference linear model made by designer and the boundedness of all signals in closed-loop system can be maintained. Therefore, we can confirm a robust adaptive control for a class of systems having continuous relay nonlinearity.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.149-152
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• 1996

In this paper, the authors propose a new method for linearizing a nonlinear dynamical system by use of Volterra kernel of the nonlinear system. The authors have recently obtained a new method for measuring Volterra kernels of nonlinear control systems by use of a pseudo-random M-sequence and correlation technique. In this method, an M-sequence is applied to the nonlinear system and the crosscorrelation function between the input and the output gives us every crosssection of Volterra kernels up to 3rd order. Once we can get Volterra kernels of nonlinear system, we can construct a linearization method of the nonlinear system. Simulation results show good agreement between the observed results and the theoretical considerations.

• International Journal of Control, Automation, and Systems
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• v.4 no.2
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• pp.178-186
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• 2006

We propose and analyze a robust adaptive fuzzy controller for nonlinear systems without a priori knowledge of the sign of the input gain function. No assumptions are made about the type of nonlinearities of the system, except that such nonlinearities are smooth. The uncertain nonlinearities are captured by the fuzzy systems that have been proven to be universal approximators. The proposed control scheme completely overcomes the singularity problem that occurs in the indirect adaptive feedback linearizing control. Projection in the estimated parameters and switching in the control input are both not required. The stability of the closed-loop system is guaranteed in the Lyapunov viewpoint.

• Journal of Electrical Engineering and Technology
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• v.11 no.2
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• pp.265-273
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• 2016

A water wall system is one of the most important components of a boiler in a thermal power plant, and it is a nonlinear Multi-Input and Multi-Output (MIMO) system, with 6 inputs and 3 outputs. Three models are developed and comp for the controller design, including a linear model, a multilayer feed-forward neural network (MFNN) model and an Echo State Network (ESN) model. First, the linear model is developed by linearizing a given nonlinear model and is analyzed as a function of the operating point. Second, the MFNN and the ESN are developed by using training data from the nonlinear model. The three models are validated using Matlab with nonlinear input-output data that was not used during training.