Generalized GTCF Coordination Mechanism Based LARC Contouring Control of Industrial Motion Stages for Complex Contours

• Published in: IEEE access Vol. 6; pp. 20067 - 20076
• Main Authors: Hu, Chuxiong, Wang, Ze, Chen, Ji, Zhu, Yu, Zhang, Ming
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive algorithms; Adaptive control; biaxial system; complex contour; Contouring; Contouring control; Contours; Control algorithms; Control theory; Coordination compounds; coordination mechanism; Curvature; generalized GTCF; Learning; Mathematical analysis; Mechatronics; motion accuracy; Motion control; Robust control; Shape; Shape functions; Synthesis; System dynamics; Task analysis; Task complexity; Trajectory
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2018.2816967

To achieve high-performance motion control accuracy for complex contouring tasks even under high-speed and large-curvature, a generalized global task coordinate frame (GGTCF)-based learning adaptive robust control (LARC) strategy is synthesized for the industrial biaxial mechatronic stage systems. Specifically, through the usually known desired trajectory information of each axis, GGTCF is firstly proposed and globally designed based on a synthesized equivalent shape function of the desired contour. Different from conventional GTCF just suitable for simple contours with explicit shape function, the proposed GGTCF can guarantee multi-axis coordination even under complex contouring tasks with highspeed and large-curvature characteristics. After transforming the system dynamics of an industrial biaxial mechatronic stage system into the proposed GGTCF, a LARC contouring controller is constructed for the strongly coupled nonlinearities in GGTCF to achieve great contouring motion performance. In LARC, adaptive compensation term and robust feedback term are, respectively, designed to deal with parametric variation and uncertain disturbances, whereas iterative learning term is designed to further suppress the unmodeled repetitive contouring errors. Comparative experiments under various complex contours are conducted on an industrial linear-motor-driven biaxial motion stage. The experimental results consistently demonstrate that the proposed GGTCF can successfully implement various complex contouring tasks. Furthermore, in comparison with the conventional cross-coupled control and adaptive robust control control algorithm, the proposed GGTCF-LARC simultaneously possesses strong coordination mechanism and excellent transient/steady-state contouring control performance, which actually provides a novel control framework with high-performance nature for contouring motion research field.