使用於多層迴路伺服馬達位置控制之速度迴路疊代學習控制器

Abstract

在工業4.0的時代背景下，智慧化機械的概念被不斷提倡，而在無人化工廠、產線全自動化的背後，是對於生產機械之精密度的更高要求，以降低即時人工調校與機械自動調校之差距。在現今常見之控制架構中，疊代學習控制器是一個架構簡單，效果顯著之控制方法，其能使用於重複執行相同命令之工作中，並透過重複執行同一命令來提升機台之控制精密度，目前被廣泛應用於機械手臂與CNC加工機上。本研究基於常見之多層閉迴路控制系統上，提出一疊代學習控制器之改良辦法，透過將傳統之疊代學習控制器由最外層之位置迴路向內移至速度迴路，提升疊代學習控制器中強健濾波器之截止頻率，使控制頻寬變高，藉此提升疊代學習控制器之控制效果。爾後，對於改良之疊代學習控制器與多層迴路控制系統整合時產生控制訊號飽和之問題，本研究也提出一改良之多層迴路控制系統以解決此問題。 改良之疊代學習控制器被實現於東台CNC車床TC-2000之XZ平台，並與常見之閉迴路及前饋控制架構、傳統疊代學習控制器比較。經過實際實驗，改良之疊代學習控制器對單軸軌跡誤差能提供30%之控制效果改善，經由循圓測試，對輪廓誤差也能提供10%之改善。

Nowadays, Industry 4.0 is the most popular issue of industrial manufacturing. As the basis of intelligent manufacturing, precision control of machine tools becomes more important than before, especially for unmanned and fully automated factories. Iterative learning control (ILC) is a commonly used control algorithm. For those machine tools that perform the same operation repeatedly and under the same operation conditions, ILC presents good control performance with a simple architecture. In this research, we improve the performance of ILC for a multi-loop position control system by allocating ILC in the velocity loop. Putting ILC into the velocity loop rises the cutoff frequency of the robust filer in the ILC and consequently a higher control bandwidth is achieved. However, this improved ILC with a conventional multi-loop control system may suffer from the problem of excessive control input. Therefore we modify the structure of the multi-loop control system to solve this problem. The improved ILC is implemented on a Tongtai CNC Lathe TC-2000 XZ table and the control performance is compared to that of traditional ILC. Data are collected from the encoder of each axis and a double ball-bar system at the XZ table. Experimental results show that the improved ILC can decrease 30% tracking error of each axis, and decrease 10% contour error in a circular test.