Offset-free Energy-optimal Model Predictive Control for point-to-point motions

Xin Wang, J. Swevers
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引用次数: 1

Abstract

This paper discusses Offset-free Energy-optimal Model Predictive Control (offset-free EOMPC) which is a MPC algorithm to realize time-constrained energy-optimal point-to-point motion control with high positioning accuracy for linear time-invariant (LTI) systems. The offset-free EOMPC approach is developed based on our previous research - Energy-optimal Model Predictive Control (EOMPC) - which aims at performing energy-optimal point-to-point motions within a given motion time. A drawback of the EOMPC method is that it cannot achieve high positioning accuracy in the presence of unmodelled disturbances or model-plant mismatch. In order to cope with this problem, a `disturbance model' strategy is adopted: the system state is augmented with disturbance variables. Based on the `disturbance model', the disturbances are estimated and the effects of which are cancelled. Experimental validation of the offset-free EOMPC on a linear motor with coulomb friction and cogging disturbances has been implemented and the results show that time-constrained energy-optimal point-to-point motion with high positioning accuracy is achieved.
点对点运动的无偏移能量最优模型预测控制
本文讨论了无偏移能量最优模型预测控制(Offset-free EOMPC)算法,它是一种用于实现线性时不变(LTI)系统具有高定位精度的时间约束能量最优点对点运动控制的MPC算法。无偏移EOMPC方法是基于我们之前的研究-能量最优模型预测控制(EOMPC) -其目的是在给定的运动时间内执行能量最优的点对点运动。EOMPC方法的缺点是在存在未建模干扰或模型-植物不匹配的情况下无法实现高定位精度。为了解决这一问题,采用了一种“扰动模型”策略:用扰动变量扩充系统状态。在“扰动模型”的基础上,对扰动进行了估计,并消除了扰动的影响。在具有库仑摩擦和齿槽扰动的直线电机上对无偏置EOMPC进行了实验验证,结果表明该方法实现了具有时间约束的能量最优点对点运动,具有较高的定位精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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