Advanced Contouring Compensation Approach via Newton-ILC and Adaptive Jerk Control for Biaxial Motion System
Hao Yuan, Ximei Zhao
Abstract
To achieve the precision contouring performance, this article presents a contouring compensation approach for the biaxial motion system even under extreme contour tasks. Two permanent magnet linear synchronous motors are installed in the biaxial motion platform. First, the dynamics of the biaxial motion servo system with the uncertainties such as model parameter variations, additive disturbance, and nonlinear friction are introduced. Since Newton algorithm is provided to realize dynamic contouring error estimation, accurate estimation of the contouring error can be achieved, even for the high velocity and complex contour with sharp corners and large curves. Then, iterative learning control (ILC) is used to control the contouring error for contouring performance improvement. An adaptive PD-type learning law based on an asymmetric Gaussian-like function is applied to improve the performance of the system. Moreover, adaptive jerk control (AJC) integrated with adaptive feedback gain is designed to address the negative effect of the uncertainties and enhance the robustness. The adaptive feedback gain is dominated and tuned by a novel adaptation law without the need for prior bound knowledge of the uncertainties. Meanwhile, parameter estimation is employed to handle the model parameter variations. Finally, the experimental results are presented to validate the efficiency and the achievable contouring performance of the proposed Newton-ILC and AJC approach.