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  • 學位論文

數據驅動馬達位置控制設計與硬體實現

Data-Driven Motor Position Control Design and Hardware Implementation

指導教授 : 周永山

摘要


數據驅動控制(Data-Driven Control, DDC)是一種特殊的控制器的設計方法,其特點為毋需建置受控體的數學模型,即可進行控制器之設計。近期在考慮量測訊號含雜訊的情況下,有一數據驅動控制方法提出線性矩陣不等式(Linear matrix inequality, LMI)形式的設計條件,求解使系統穩定的狀態回授增益。本論文基於此方法,予以推廣至步階追蹤控制。以MATLAB進行直流馬達定位控制以及倒單擺平衡控制的數值模擬;另外也進行直流馬達定位控制的硬體實驗。結果證實所提出數據驅動控制設計的可行性。

並列摘要


Data-Driven Controls are a special class of controller design methods. They are characterized that the controllers can be designed directly by the data collected from a single open-loop trajectory of the plant without the need of building a mathematical model for the plant. Recently, a DDC method using noisy data was presented where the solvability condition for the existence of a stabilizing state feedback gain is in the form of linear matrix inequality (LMI). This thesis extends the existing stabilizing design to step tracking control. Numerical simulation for controlling the position of a DC motor and balancing an inverted pendulum using Matlab are conducted. In addition, hardware implementation of the DC motor position control system is established, which verifies the effectiveness of the proposed DDC design.

參考文獻


[1] G. R. G. da Silva , A. S. Bazanella , C. Lorenzini, and L. Campestrini,“ Data-Driven LQR Control Design,” IEEE Control Syst. Lett., vol. 3, no. 1, pp. 180-185, 2019.
[2] C. De Persis and P. Tesi, “Formulas for Data-Driven Control: Stabilization, Optimality, and Robustness,” IEEE Trans. Autom. Control, vol. 65, no. 3, pp. 909-924, 2020.
[3] J. Berberich, A. Koch, C. W. Scherer, and F. Allgower, “Robust data-driven state-feedback design,” in Proc. American Control Conference, pp. 1532-1538, 2020.
[4] H. J. van Waarde, M. K. Camlibel, M. Mesbahi, “From Noisy Data to Feedback Controllers: Nonconservative Design via a Matrix S-Lemma,” IEEE Trans. Autom. Control, vol. 67, no. 1, pp. 162-175, 2022.
[5] H. J. van Waarde and M. K. Camlibel, “A Matrix Finsler’s Lemma with Applications to Data-Driven Control,” 2021 60th IEEE Conference on Decision and Control, 2020, pp.5777-5782.

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