Model Predictive Approach to Precision Contouring Control for Feed Drive Systems
M.A. El Khalick and N. Uchyiama
DOI : 10.3844/jcssp.2010.844.851
Journal of Computer Science
Volume 6, Issue 8
Problem statement: High precision machining requires high capability of multi-axis feed drive systems to follow specified contour accurately. Although each feed drive axis is controlled independently in many industrial applications such as X-Y tables and Computer Numerical Control (CNC) machines, machining precision is evaluated by error components orthogonal to desired contour curve. Contouring controller design is required for precision machining, which should consider disturbance and dynamics variation such as friction, cutting force and workpiece mass change. Approach: This study applied model predictive design to contouring control systems. Model predictive control utilized an explicit process model and tracking error dynamics to predict the future behavior of a plant and hence it is effective for precision machining in machine tool feed drives. To improve the contouring performance, a new performance index was proposed in which error components orthogonal to the desired contour curve are more important than tracking errors with respect to each feed drive axis. Controller parameters were calculated in real time by solving an optimization problem. Results: The proposed controller was evaluated by computer simulation for circular and non-circular trajectories. Weighting factors of performance index terms were used as tuning factors of the proposed controller. Simulation results showed that a better contouring performance can be obtained by choosing of the weighting factors in performance index items appropriately. Conclusion/Recommendations: A model predictive contouring controller for biaxial feed drive systems was presented. Simulation results demonstrated that the proposed approach can significantly improve the contouring accuracy.
© 2010 M.A. El Khalick and N. Uchyiama. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.