| Summary: | 碩士 === 國立中正大學 === 前瞻製造系統碩士學位學程 === 106 === Helical interpolation machining is a widely used technique in manufacturing and arms industries to create helical contours or take advantages of this technique in machining processes.
In general, reducing helical contour errors is a difficult task because the helix is a 3 -dimensional curve, making it extremely hard to estimate the actual contour error through individual axis-tracking errors. In recent years, iterative learning control (ILC) has been successfully applied to precision motion controllers specializing in repeated tasks. Conventionally, most of them have focused on reducing axis-tracking errors, however, that cannot guarantee to obtain smaller actual contour errors in general.
To overcome these difficulties, a new concept “equivalent contour error” model is taken as our control objective instead of using the complex actual contour model. In this study, an online ILC framework will be introduced to gradually enhance helical contours via learning processes by adjusting input commands. The online terminology meaning that after data coming, we simultaneously update the input command for the next learning iteration at each time step. This
online technique avoids the batch learning process of so-called offline ILC, which consumes a
huge amount of memory to save collected data along with high computation time.
In particular, our proposed control law is able not only to iteratively reduce the control objective but also to deal with initial state errors problem resulting from different initial states at each learning iteration. Furthermore, in our learning control framework, we employ a fuzzy decision support system to adaptively select local learning convergence rates for speeding up the learning process and preventing the noise amplification phenomenon.
Practically, we employ a PC-based controller board connected to a real CNC machine to control feed-drive systems. Our algorithm is implemented in C-programming language, which is one of the fastest computing languages, and optimally organized in terms of data structures.
Finally, experimental results validate our proposed online iterative learning control framework and
verify the feasibility of integrating the advanced control function into real precision motion
controllers.
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