Abstract
In this paper, a model predictive control algorithm is developed for the regulation problem of a biaxial feed drive system. The orthogonal error component in the moving frame is considered as an approximation of the real contour error. Then, the control policy is derived from the worst-case optimization of a quadratic cost function, which penalizes transformed errors, velocity errors and control variables in each sampling time over a finite horizon. In addition, the constraint is satisfied to ensure the convergence against uncertain but bounded disturbances. The good performance of the proposed control algorithm is verified via computer simulations with predefined trajectories. Furthermore, the result shows the improvement of the tracking accuracy by comparing with the unconstrained predictive control methods.
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Yu Gao received his Ph.D. degree in electronics engineering from Chonbuk National University, Korea, in 2012. He is a lecturer in the School of Mechanical and Electrical Engineering, Soochow University. His research interests include model predictive control, optimal control, and robust control.
Jun Huang was born in Anhui Province, China, in 1984. He obtained his M.S. degree in mathematics from East China Normal University in 2008 and his Ph.D. degree in automation from Shanghai Jiao Tong University in 2012. He is now an associate professor in the School of Mechanical and Electrical Engineering, Soochow University. His current research interests include uncertain control systems, interval observer design, consensus of multi-agent.
Liang Chen received his Ph.D. degree in control engineering from a joint Ph.D. program at Zhejiang University & TU Berlin in 2009. He is now an Associate Professor and the head of Department of Automation Engineering at the School of Mechanical and Electric Engineering, Soochow University. His research interests include intelligent robots and deep learning based intelligent control.
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Gao, Y., Huang, J. & Chen, L. Constrained Model Predictive Contour Error Control for Feed Drive Systems with Uncertainties. Int. J. Control Autom. Syst. 19, 209–220 (2021). https://doi.org/10.1007/s12555-019-0915-6
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DOI: https://doi.org/10.1007/s12555-019-0915-6