LAUSR

This paper proposes a Newton extremum seeking algorithm based iterative learning coordinated control (Newton-ILC) strategy for contouring motion accuracy of precision multiaxial systems. Specifically, as the contouring error estimation is critically important for coordinated contouring motion control, a cost function is constructed based on the reference contour, as well as the current position, and the minimal value of the function is searched to determine the contouring error point through an offline Newton algorithm. Consequently, high precision estimation of the contouring error can be achieved, even under extreme contouring tasks with high speed, large curvature, and sharp corners. The calculated contouring error is then projected to each axis, and the axial contouring errors are controlled by iterative learning method, while the learning results will be used to adjust the axial position reference commands for contouring accuracy improvement. Comparative experiments are conducted on a biaxial linear motor stage to validate the practical effectiveness of the proposed Newton-ILC strategy. The experimental results illustrate that the proposed Newton-ILC achieves not only nearly perfect contouring error estimation but obvious improvement of contouring accuracy as well after a few iterations. Particularly, in some extreme cases such as large initial tracking error, high speed, large curvature, and sharp corners, the proposed Newton-ILC strategy can achieve rather excellent contouring performance when compared with individual axis control, conditional cross-coupled control, and cross-coupled iterative learning coordinated control (CCILC) methods.