龍門式雙軸平台之模糊類神經網路同步控制

Fuzzy Neural Network Synchronous Control of Gantry Stage

楊宗翰(Tsung-Han Yang)

指導教授 ： 陳金聖

國立臺北科技大學/機電學院/自動化科技研究所/碩士(2011年)

摘要

許多高負載之平台為了增加單一軸向的推力，開始採用龍門式雙軸平台架構來做運動控制，在高速運動下若各軸伺服系統之間的同步誤差過大，則會因為機構耦合的關係而造成兩邊的驅動軸機構產生拉扯的力量，導致機構變形或損毀，因此如何有效的精準驅動工作平台達成同步運動，會是一個很重要的議題。
　　單軸馬達移動平台本身存在著參數變動與外力干擾，本論文首先利用參考模型適應性控制來補償單軸系統鑑別之不準確度與外力干擾。然而，龍門式雙軸平台因為兩軸不同步，造成機構耦合部分有複雜的非線性行為難以模型化，甚至模型化有相當大的不準確度而無法線性化，因此本論文再提出模糊類神經網路(Fuzzy Neural Network)補償控制與線上學習(On-line Learning)調整法則來解決。模糊類神經網路是對輸入訊號(兩驅動軸的位置差、速度差)進行合理的模糊劃分，經由神經網路連結傳遞到輸出，並把此輸出的控制量補償回各軸的控制器。而線上學習調整法則是利用監督式梯度遞減法來調整網路架構中的連結權重值，使定義的誤差函數(E)最小化。最後設計低速與高速正弦形式位置命令來實驗，從實驗結果證實所提出之同步控制架構具有可行性。

關鍵字

模糊類神經網路 ； 線上學習調整法則 ； 龍門式雙軸平台 ； 同步控制

並列摘要

To improve a single-axial driving force, many high-load stages start adopting gantry stage architecture in different industry application. If the synchronous error among dual-drive servo systems at a high-speed motion is too large, then the mechanical coupling force yielded on both servo systems will result in a mechanical deformation or damage. Therefore, it is an important issue to find a way to drive the stage to achieve a synchronous motion effectively and precisely.
　　For single-axial servo motor, the existing parameter variations and external disturbance will degenerate the control performance. First, the model reference adaptive control (MRAC) is proposed to compensate the parameter uncertainty, yielded by the inaccuracy of parameter identification, and external disturbance. Since the two axes of the gantry stage are asynchronous, it is difficult to model the complicated non-linear behavior of mechanical coupling phenomenon. This thesis proposes a fuzzy neural network (FNN) compensation control and an on-line learning algorithm to overcome the aforementioned problem. The fuzzy neural network performs a reasonable fuzzy partition to both synchronous position and velocity errors between two dual-drive servo systems and transmits the parted signals to generate the compensated force via neural network reasoning, and the compensated force is fed back to the controller of each axis. The on-line learning algorithm adjusts the connected weighting of the neural network by using a supervised gradient descent method, such that the defined error function(E) can be minimized. Finally, two kinds of low-speed and high-speed sinusoidal position commands are designed for the experiments, and the experimental results show that the proposed MRAC and FNN control scheme are feasible to improve the single-axis and synchronous control, respectively.

並列關鍵字

Fuzzy Neural Network ； Online Learning Adjustment Algorithm ； Gantry Stage ； Synchronous Control

參考文獻

[1] 楊君賢，具機構耦合之雙線性伺服系統鑑別與控制，碩士論文，國立成功大學機械工程學系，台南，2003。

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