• Title/Summary/Keyword: K-linearization

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On Feedback Linearization of Nonlinear Time-Delay Systems

  • Shin, Hee-Sub;Lim, Jong-Tae
    • 제어로봇시스템학회:학술대회논문집
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    • 2004.08a
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    • pp.1906-1908
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    • 2004
  • We propose a result on the stabilization of nonlinear time-delay systems via the feedback linearization method. Using the predictor based control and the parametric coordinate transformation, we introduce a stabilizing controller to compensate time delay. Specifically, we present the delay-dependent stability analysis to makes the considered system stable. Also, an illustrative example is provided

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A formal linearization method via cubic splines and its applications

  • Narikiyo, Katsuhiro;Takata, Hitoshi
    • 제어로봇시스템학회:학술대회논문집
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    • 1991.10b
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    • pp.1848-1853
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    • 1991
  • To solve the nonlinear system problems, many methods have been proposed. Generally those methods however need long processing time because of their complicated algorithms. On the other hand, some simple linearization methods also have been studied. In this paper, a new linearization method using cubic splines[1] is proposed. The approximated linear system obtained by this method we can apply the conventional simple linear system theories such as Kalman filter[2, 3] for the estimation problem.

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A formal linearization of nonlinear systems based on the discrete-fourier transform

  • Takata, Hitoshi;Komatsu, Kazuo
    • 제어로봇시스템학회:학술대회논문집
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    • 1991.10b
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    • pp.1823-1827
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    • 1991
  • The problem regarding nonlinear systems has come to occupy an important position. In order to solve a nonlinear problem we have methods of linearization which are developed through linear approximation to adapt linear system theories. In this paper we present a formal linearization of nonlinear systems based on the discrete-Fourier transform (D.F.T.).

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A New Statistical Linearization Technique of Nonlinear System (비선형시스템의 새로운 통계적 선형화방법)

  • Lee, Jang-Gyu;Lee, Yeon-Seok
    • Proceedings of the KIEE Conference
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    • 1990.07a
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    • pp.72-76
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    • 1990
  • A new statistical linearization technique for nonlinear system called covariance matching method is proposed in this paper. The covariance matching method makes the mean and variance of an approximated output be identical real functional output, and the distribution of the approximated output have identical shape with a given random input. Also, the covariance matching method can be easily implemented for statistical analysis of nonlinear systems with a combination of linear system covariance analysis.

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A Feedback Linearization Control of Container Cranes: Varying Rope Length

  • Park, Hahn;Chwa, Dong-Kyoung;Hong, Keum-Shik
    • International Journal of Control, Automation, and Systems
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    • v.5 no.4
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    • pp.379-387
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    • 2007
  • In this paper, a nonlinear anti-sway controller for container cranes with load hoisting is investigated. The considered container crane involves a planar motion in conjunction with a hoisting motion. The control inputs are two (trolley and hoisting forces), whereas the variables to be controlled are three (trolley position, hoisting rope length, and sway angle). A novel feedback linearization control law provides a simultaneous trolley-position regulation, sway suppression, and load hoisting control. The performance of the closed loop system is shown to be satisfactory in the presence of disturbances at the payload and rope length variations. The advantage of the proposed control law lies in the full incorporation of the nonlinear dynamics by partial feedback linearization. The uniform asymptotic stability of the closed-loop system is assured irrespective of variations of the rope length. Simulation and experimental results are compared and discussed.

Intelligent Control of Nuclear Power Plant Steam Generator Using Neural Networks (신경회로망을 이용한 원자력발전소 증기발생기의 지능제어)

  • Kim, Sung-Soo;Lee, Jae-Gi;Choi, Jin-Young
    • Journal of Institute of Control, Robotics and Systems
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    • v.6 no.2
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    • pp.127-137
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    • 2000
  • This paper presents a novel neural based controller which controls the water level of the nuclear power plant steam generator. The controller consists of a model reference feedback linearization controller and a PI controller for stabilizing the feedback linearization controller. The feedback linearization controller consists of a neural network model and an inversing module which uses the neural network model for computing the control input to the steam generator. We chose Piecewise Linearly Trained Network(PLTN) and Recurrent Neural Netwrok(RNN) for an approximator of the plant and used these approximators in calculating the input from the feedback linearization controller. Combining the above two controllers gives a result of better performance than the case which uses only a PI controller Each control result of PLTN and RNN is given.

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Sliding Mode Control with the feedback linearization and novel sliding surface for induction motors (새로운 슬라이딩 평면과 궤환 선형화를 이용한 유도 전동기의 슬라이딩 모드 제어)

  • Park, Seung-Kyu;Ahn, Ho-Kyun;Kim, Hyung-Moon
    • Proceedings of the KIEE Conference
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    • 2000.07d
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    • pp.2672-2674
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    • 2000
  • In this paper. feedback linearization and the sliding mode control(SMC) are used together for uncertain nonlinear system. An advantage of feedback linearization technique is to make linear control theories can be used for nonlinear system and the SMC have the robustness. But the dynamics of the SMC has the dynamics lower order than that of the original system. Therefore the linear control theory can not be used with the SMC. The novel sliding surface of the SMC can have the dynamics of the nominal non linear system controlled by the feedback linearization. The proposed method can be used for the control of induction motors.

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An Improved Poincaré-like Carleman Linearization Approach for Power System Nonlinear Analysis

  • Wang, Zhou-Qiang;Huang, Qi;Zhang, Chang-Hua
    • Journal of Electrical Engineering and Technology
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    • v.8 no.2
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    • pp.271-281
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    • 2013
  • In order to improve the performance of analysis, it is important to consider the nonlinearity in power system. The Carleman embedding technique (linearization procedure) provides an effective approach in reduction of nonlinear systems. In the approach, a group of differential equations in which the state variables are formed by the original state variables and the vector monomials one can build with products of positive integer powers of them, is constructed. In traditional Carleman linearization technique, the tensor matrix is truncated to form a square matrix, and then regular linear system theory is used to solve the truncated system directly. However, it is found that part of nonlinear information is neglected when truncating the Carleman model. This paper proposes a new approach to solve the problem, by combining the Poincar$\acute{e}$ transformation with the Carleman linearization. Case studies are presented to verify the proposed method. Modal analysis shows that, with traditional Carleman linearization, the calculated contribution factors are not symmetrical, while such problems are avoided in the improved approach.

Adaptive Feedback Linearization Control Based on Stator Fluxes Model for Induction Motors

  • Jeon, Seok-Ho;Park, Jin-Young
    • Transactions on Control, Automation and Systems Engineering
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    • v.4 no.4
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    • pp.253-263
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    • 2002
  • This paper presents an adaptive feedback linearization control scheme for induction motors using stator fluxes. By using stator flukes as states, overparameterization is prevented and control inputs can be determined straightforwardly unlike in existing schemes. This approach leads to the decrease of the relative degree for the flux modulus and thus yields a simpler control algorithm than the prior results. In this paper. adaptation schemes are suggested to compensate for the variations of stator resistance. rotor resistance and load torque. In particular, the adaptation to the variation of stator resistance with a feedback linearization control is a new trial. In addition, to improve the convergence of rotor resistance estimation, the differences between stator currents and its estimates are used for the parameter adaptation. The simulations show that torque and flux are controlled independently and that the estimates of stator resistance, rotor resistance, and load torque converge to their true values. Actual experiments on a 3.7㎾ induction motor verify the effectiveness of the proposed method.