Abstract
ABSTRACTOne of the most popular applications of a bi-axial motion stage is precision motion control. The reduction of tracking error and contour error is one of the most coveted goals in precision motion control systems. The accuracy of a motion control system is often affected by external disturbances. In addition, system non-linearity such as friction also represents a major hurdle to motion precision. In order to deal with the aforementioned problem, this paper proposes a fuzzy logic-based Reinforcement Iterative Learning Control (RILC) and a Cross-Coupled Cerebellar Model Articulation Controller (CCCMAC). In particular, the proposed fuzzy logic-based RILC and a LuGre friction model-based compensation approach are exploited to improve motion accuracy. The fuzzy logic-based RILC aims at reducing tracking error and compensating for external disturbance, while the LuGre friction model is responsible for friction compensation. In addition, the CCCMAC consisting of a cerebellar model articulation controller and a cross-coupled controller aims at reducing contour error and dealing with the problem of dynamics mismatch between different axes. Performance comparisons between the proposed fuzzy logic-based Reinforcement Iterative Learning Cross-Coupled Cerebellar Model Articulation Controller (RIL–CCCMAC) and several existing control schemes are conducted on a bi-axial motion stage. Experimental results verify the effectiveness of the proposed RIL–CCCMAC.
Highlights
Due to the thriving development of the Information Technology (IT) industry, the Computer Numerical Control (CNC) machine tools used in manufacturing IT products have been in high demand
In order to deal with the aforementioned problem, this paper proposes a fuzzy logic-based Reinforcement Iterative Learning Control (RILC) and a Cross-Coupled Cerebellar Model Articulation Controller (CCCMAC)
The fuzzy logic-based RILC aims at reducing tracking error and compensating for external disturbance, while the LuGre friction model is responsible for friction compensation
Summary
Due to the thriving development of the Information Technology (IT) industry, the Computer Numerical Control (CNC) machine tools used in manufacturing IT products have been in high demand. In order to enhance the applicability of the CCC, many researchers have developed different kinds of contour error estimation algorithms that are suitable for non-linear trajectories or even free-form parametric curves [9,10]. This paper will incorporate the parameter-based contour error estimation algorithm into the CCC for application to different types of trajectories, as well as improve the effectiveness of the CCC. Due to the rapid development of artificial neural networks and other learning approaches for the past several decades, more and more learning control algorithms are exploited to improve contour-following accuracy of multi-axis motion stages [12,13,14,15,16,17,18]. The proposed CCCMAC can deal with the dynamics incompatibility problem, it may not be as effective when coping with periodic external disturbance and non-linearities such as friction resulting from repetitive contour-following motions.
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