Iterative learning contouring control: Theory and application to biaxial systems

• Published in: Mechatronics (Oxford) 2023-02, Vol.89, p.102932, Article 102932
• Main Authors: Chen, Shyh-Leh, Hsieh, Sheng-Min
• Format: Article
• Language: English
• Subjects: Contouring control; Equivalent contour errors; Iterative learning control; Motion control; Multi-axis motion systems
• ISSN: 0957-4158; 1873-4006
• DOI: 10.1016/j.mechatronics.2022.102932

•Novel iterative learning control algorithm for the contouring control of multi-axis motion system is presented.•The concept of equivalent contour error is integrated into the iterative learning control algorithm.•The contour error can be directly reduced, without the need of accurate or approximate. This paper is concerned with the iterative learning control (ILC) for the contouring control of multi-axis motion system. A novel ILC algorithm, which is called iterative learning contouring control (ILCC), is proposed and verified with biaxial motion systems. In motion control, conventional ILC requires that the number of control objectives is the same as the number of command inputs, which is called square property. This allows for the formulation of transfer function or matrix inversion. The unique feature of contouring control is its non-square property. For contouring control, conventional ILC circumvents the non-square property by adopting the configuration of cross-coupled control (CCC). In other words, the estimated contour error is decomposed into components of individual axis for compensation by ILC and thus the contour error is reduced indirectly. The proposed ILCC can deal with the non-square property and can directly reduce the contour error by adopting the equivalent contour error as the control objective of the ILC. The equivalent contour error represents an accurate contour error model and hence better contouring accuracy can be achieved. The ILCC algorithm is implemented on two platforms, an XY table and a commercial CNC machine tool. Experiments on the contouring control of a circular path with different speeds and a B-spline path are conducted. It is found that the contour error can be reduced about 88% after several learning iterations. The experimental results confirm the effectiveness of the proposed method and show the excellent contouring performance. [Display omitted]