Tracking Error Reduction of Biaxial Motion Control Systems-An ILC-CMAC Approach

基於反覆學習控制與小腦模型控制器之雙軸運動控制系統追蹤誤差降低方法

郭威良(Wei-Liang Kuo) ； 鄭銘揚(Ming-Yang Cheng) ； 蔡維哲(Wei-Che Tsai)

《中國機械工程學刊》 38卷1期 (2017/02) Pp. 13-22

摘要

Reduction of tracking error is one of the most important issues in contour following of bi-axial motion control systems. In general, contour following accuracy is affected by system nonlinearities and external disturbances. In order to reduce tracking error so as to improve contour following accuracy, developing a motion control scheme that can exhibit excellent tracking performance is essential. As a result, this paper proposes a motion control scheme that combines a Cerebellar Model Articulation Controller (CMAC) with an Iterative Learning Control (ILC) scheme. In particular, CMAC acts as a feedforward compensator to cope with system nonlinearity such as friction, while ILC is used in the feedback loop to suppress periodic disturbance. Several bi-axial contour following experiments have been conducted to verify the feasibility of the proposed motion control approach. Experimental results indicate that the proposed approach outperforms the existing control schemes also tested in the experiment.

關鍵字

iterative learning control ； cerebellar model articulation controller ； bi-axial motion stage

並列摘要

如何降低追蹤誤差一直是雙軸運動控制平台進行循跡運動時的重要議題。一般而言，循跡精度經常受到系統之非線性以及外部干擾量所影響。為了降低追蹤誤差進而提升循跡控制精度，發展一具有優異追蹤性能之運動控制架構就顯得格外重要。因此，本文提出一整合了類神經小腦模型控制器(Cerebellar Model Articulation Controller)及反覆學習控制器(Iterative Learning Controller)的運動控制架構。其中，CMAC類似扮演前饋控制器，主要用以補償系統非線性如摩擦力等，而ILC則用於回授迴路以抑制系統的外部干擾量。數個雙軸運動平台循跡實驗被用來驗證本文所提出控制架構之可行性。實驗結果顯示本文所提出控制架構之性能優於其他也接受循跡實驗測試之控制架構。