Sensorless Anti-swing Robust Nonlinear Control for Travelling Crane System Using SVR with Generalized Gaussian Function and Robust Right Coprime Factorization

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  • DENG Mingcong
    The Graduate School of Engineering, Tokyo University of Aguriculture and Technology
  • WEN Shengjun
    The Graduate School of Engineering, Tokyo University of Aguriculture and Technology
  • INOUE Akira
    The Graduate School of Natural Science and Technology, Okayama University

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Other Title
  • 一般化ガウス関数を用いたSVRによるセンサーレス走行クレーンのロバスト右既約分解によるロバスト非線形制御
  • イッパンカ ガウス カンスウ オ モチイタ SVR ニ ヨル センサーレス ソウコウ クレーン ノ ロバスト ウ キヤク ブンカイ ニ ヨル ロバスト ヒセンケイ セイギョ

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In this paper, a sensorless robust nonlinear control for travelling cranes is proposed. The control objectives are to control two outputs, the dispalcement of the travelling trolley and swing angle of the pay load using one input force, that is, the control system is an underactuated system. To control the outputs, a real-time measurement of the outputs is neccessary. But the real-time measurement of the swing angle using hardware sensors is difficult and a software method for the measurement is required. However, the crane system is described as a nonlinear dynamic system including uncertainty, which represents estimation errors of motion friction and viscous friction and unmodeled dynamics. Because of the uncertainty, it is difficult to estimate swing angle of the payload using model-based methods. This paper uses data-based Support Vector Regression for dealing with the above problem. And to enhance the generalizing ability and to apply to estimation of the swing angle in this system, this paper newly proposes to use a generalized Gaussian function as a kernel fucntion and real-time estimation of the two parameters of the function, that is, variance and shape parameter of the generalized Gaussian function. As the control law, two feedback loops are used. First is robust nonlinear stabilizing control law based on right coprime factorization approach. Second is given outside of the first feedback loop and it is nonlinear tracking controller. For the tracking controller, an optimization scheme is derived. Finally, experimental results are given to show the effectiveness of the proposed scheme.

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