基於反覆學習控制之機械手臂的即時繪圖系統

Real-time Drafting System for Robot Manipulator Based on Iterative Learning Control

汪培聖(Pei-Sheng Wang)

指導教授 ： 周永山

淡江大學/工學院/電機工程學系碩士班/碩士(2018年)

https://doi.org/10.6846/TKU.2018.00988

摘要

本論文之主要目的在於設計一個基於反覆學習控制之機械手臂即時繪圖系統。此系統結合影像處理讓使用者可以用更簡單的方式與更少的時間達成繪製路徑的任務。本論文繪圖系統之設計包含影像處理與路徑追蹤控制兩部分。在影像處理部分，透過影像處理技術取得之目標路徑。在路徑追蹤部分，本論文控制機械手臂末端點加速度以規劃路徑，讓路徑更貼近實際目標，毋需再經過額外的路徑規劃。控制參數及依據路徑追蹤誤差收斂條件求解，其為線性矩陣不等式(Linear Matrix Inequality, LMI)，以軟體解出控制器之相關參數。依此方式設計的反覆學習控制器，與過往方法相比更具可靠性。此外，本論文亦發現如何透過改變反覆學習控制演算法之參數限制條件，以達到較佳之收斂效果。在實驗結果方面，透過反覆學習演算法控制器進行實驗。驗證本論文所提出之演算法設計確實能有效地使得路徑追蹤誤差收斂。

關鍵字

反覆學習控制 ； 機械手臂 ； 路徑追蹤 ； 影像處理

並列摘要

This thesis is concerned with the design of an iterative-learning-control (ILC)-based robot arm drawing system. This system combined with image processing allows users to achieve the task of drawing paths faster and in a simpler way.
The design of the drawing system of this thesis includes two parts: image processing and path tracking control. In the image processing section, the target path was obtained through image processing. In the path tracking section, the motion of the end point of the robot arm was control via adjusting its acceleration. This allows the resulting path to be closer to the target path without additional path planning.
The control parameters were computed based on the derived condition for error convergence. It is in the form of linear matrix inequality (LMI) which can be efficiently solved via existing software. Compared to traditional methods where the control parameters were obtained in an ad hoc fashion, the proposed method produces controllers which are theoretical-based. In addition, some rules that can achieve better convergence were deduced by a serial simulation by changing the design parameters. Numerical experiment shows that the proposed ILC-based controller does effectively make the tracking error converge to a satisfactory level.

並列關鍵字

Iterative Learning Control (ILC) ； Robot Manipulator ； Path Tracking ； Image Processing

參考文獻

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