• Journal of Power Electronics
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• v.16 no.1
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• pp.351-361
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• 2016

Power quality is a critical issue in distribution systems, where a dynamic voltage restorer (DVR) is commonly used to mitigate the voltage disturbances for loads. This paper deals with a nonlinear control for the three-phase four-wire (3P-4W) DVR under a grid voltage unbalance and nonlinear loads in the distribution system, where a novel control scheme based on the feedback linearization technique is proposed. Through feedback linearization, a nonlinear model of a DVR with a PWM voltage-source inverter (VSI) and LC filters is linearized. Then, the controller design of the linearized model is performed by applying the linear control theory, where the load voltages are kept constant by controlling the d-q-0 axis components of the DVR output voltages. To keep the load voltage unchanged, an in-phase compensation strategy is employed, where the load voltages are recovered to be the same as the previous voltage without a change in the magnitude. With this strategy, the performance of the DVR becomes faster and more stable even under unbalanced source voltages and nonlinear loads. The validity of the proposed control strategy has been verified by simulation and experimental results.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.2
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• pp.94-101
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• 1996

The equations of motion for a basic industrial robotic system which has a rigid or a flexible arm are derived by Lagrange's equation, respectively. Especially, for the deflection of the flexible arm, the assumed mode method is employed. These equations are highly nonlinear equations with nonlinear coupling between the variables of motion. In order to design the control law for the rigid-arm robot, Hunt-Su's nonlinear transformation method and Marino's feedback equivalence condition are used with linear quadratic regulator(LQR) theory. The control law for the rigid-arm robot is employed to input the desired path and to provide the required nonlinear transformations for the flexible-arm robot to follow. By using the implicit Euler method to solve the nonlinear equations, the comparison of the motions between the flexible and the rigid robots and the effect of flexibility are examined.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.557-560
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• 2005

This paper considers a global asymptotic output tracking problem with a prescribed constant reference signal for a class of single-input and single output-output nonlinear systems. It is shown that under some mild conditions on such a system there is a smooth output feedback achieving global asymptotic output tracking and such a smooth output controller is explicitly constructed by a new design method proposed. The usefulness of our result is illustrated by a numerical example.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.172.3-172
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• 2001

In this paper, we find the necessary and sufficient conditions of linearization of nonlinear discrete-time systems with 2 inputs using the restricted class of dynamic feedback. That is, this paper is the discrete version of [2]. The results we obtain for discrete-time nonlinear systems are, however, quite different from that of continuous-time case.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.967-969
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• 1998

In this paper, the proposed controller is obtained by combining the nonlinear feedback linearization-sliding mode controller (NFL-SMC) with the full order observer (FOO) and eliminates the need to measure all the state variables in the conventional NFL-SMC.

• Journal of IKEEE
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• v.14 no.3
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• pp.182-189
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• 2010

A new Adaptive neural state-feedback controller for the fully nonaffine pure-feedback nonlinear system are presented in this paper. By reformulating the original pure-feedback system to a standard normal form with respect to newly defined state variables, the proposed controller requires no backstepping design procedure. Avoiding backstepping makes the controller structure and stability analysis considerably simple. The proposed controller employs only one neural network to approximate unknown ideal controllers, which highlights the simplicity of the proposed neural controller. Simulation examples demonstrate the efficiency and performance of the proposed approach.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.161-164
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• 1996

The authors have recently proposed a new method for identifying Volterra kernels of nonlinear control systems by use of M-sequence and correlation technique. A specially chosen M-sequence is added to the nonlinear system to be identified, and the crosscorrelation function between the input and output is calculated. Then every crosssection of Volterra kernels up to 3rd order appears at a specified delay time point in the crosscorrelation. This method is applied to a saturation-type nonlinear feedback control system of mechanical-electrical servo system having torque saturation nonlinearity. Simulation experiments show that we can obtain Volterra kernels of saturation-type nonlinear system, and a good agreement is observed between the observed output and the calculated one from the measured Volterra kernels.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.3
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• pp.46-54
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• 1997

In this paper, we psropose the feedback linearization technique for a nonlinear system using genetic algorithms (GAs) and fuzzy logic system. The proposed control scheme approximates the nonlinear term of a nonlinear system using the fuzzy logic system and computes the control input for cancelling the nonlinear term. Then in the fuzzy logic system, the number and shape of membership function of the premise aprt will be tuned to minimize the control error boundary using GAs. And the parameters of the consequence of fuzzy rule will be tuned by the adaptive laws based on lyapunov stability theory in order to guarantee the closed loop stability of control system. The evolution of fuzzy logic system is processed during the on-line adaptive control. The effectiveness of proposed method will be demonstrated by computer simulation of simple nonlinear sytem.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.5
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• pp.608-615
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• 1998

In this paper a second-order iterative learning control algorithm with feedback is proposed for the trajectory-tracking control of nonlinear dynamic systems with unidentified parameters. In contrast to other known methods, the proposed teaming control scheme utilize more than one past error history contained in the trajectories generated at prior iterations, and a feedback term is added in the learning control scheme for the enhancement of convergence speed and robustness to disturbances or system parameter variations. The convergence proof of the proposed algorithm is given in detail, and the sufficient condition for the convergence of the algorithm is provided. We also discuss the convergence performance of the algorithm when the initial condition at the beginning of each iteration differs from the previous value of the initial condition. The effectiveness of the proposed algorithm is shown by computer simulation result. It is shown that, by adding a feedback term in teaming control algorithm, convergence speed, robustness to disturbances and robustness to unmatched initial conditions can be improved.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.4
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• pp.221-227
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• 2000

This paper combines the constrained model predictive control with the feedback linearization to solve a nonlinear system control problem with input constraints. The combined approach consists of two steps: Firstly, the nonlinear model is linearized by the feedback linearization. Secondly, based on the linearized model, the constrained model predictive controller is designed taking input constraints into consideration. The proposed controller is applied to two link robot system, and tracking performances of the controller are investigated via some simulations, where the comparisons are done for the cases of unconstrained, constrained input in feedback linearization.