システム制御情報学会論文誌
Online ISSN : 2185-811X
Print ISSN : 1342-5668
ISSN-L : 1342-5668
33 巻, 7 号
選択された号の論文の2件中1~2を表示しています
論文
  • 箕土路 拓也, 長瀬 賢二
    2020 年 33 巻 7 号 p. 207-218
    発行日: 2020/07/15
    公開日: 2020/10/15
    ジャーナル オープンアクセス

    This paper considers wave analysis and control of double cascade-connected damped mass-spring systems whose mass is connected to two adjacent masses on both sides. As an extension of our previous research on the uniform case, physical parameters of the system are assumed to vary along the layers. For the wave analysis, we require for the propagation constants to be independent of the layer position, and show that the physical parameters should vary uniformly along the layer. For the system satisfying the condition, properties of the secondary constants (propagation constants, characteristic admittances) and the transformation matrix as analytic functions are examined in detail. Under a weak condition (inequality) on two springs and dampers in the same layer, we show that the secondary constants and the transformation matrix satisfy the required properties for the wave analysis (analyticity on the open right half-plane, positive real property of the characteristic admittances, etc.). Validity of the derived results and efficiency of the impedance matching controller for vibration control are illustrated in the numerical example.

  • 吉村 優一, 橋本 和宗, 潮 俊光
    2020 年 33 巻 7 号 p. 219-228
    発行日: 2020/07/15
    公開日: 2020/10/15
    ジャーナル オープンアクセス

    In networked control systems, it is an important issue to reduce communication costs using aperiodic communication. In this paper, we propose a method to design the event-triggered controllers, which contains the control policy and the communication policy, for uncertain discrete-time nonlinear systems with disturbances. The control policy determines the control inputs to drive the system to the target state, and that the communication policy determines whether the control inputs should be transmitted or not based on the communication cost and the control performance. In the proposed method, two Gaussian Process models represent the uncertainty in dynamics and a state-valued function to design controllers using policy iteration. We demonstrate the numerical example to show how our learning algorithm works.

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