A discrete-time norm-optimal approach to iterative learning control of a bridge crane

H. Aschemann, A. Wache, Ole Kraegenbring
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引用次数: 2

Abstract

In this contribution, a norm-optimal iterative learning control (NOILC) for the two main axes of a bridge crane is presented. For each axis, the NOILC operates in parallel to a linear-quadratic (LQ) state feedback of the tracking error. Regarding the tracking of repetitive trajectories, the ILC part contributes to a significant reduction of the tracking error from iteration to iteration, up to an accuracy that is determined by the quality of the measurement signals. In this paper, the ILC law is based on the minimization of a cost functional and involves both feedforward and feedback control actions. The control structure has been implemented at a bridge crane test rig with three axes, where the lateral rope deflections are determined by means of a CMOS camera. Experimental results show that a fast error convergence and a small remaining tracking error can be achieved with the proposed control structure.
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桥式起重机迭代学习控制的离散范数最优方法
本文提出了桥式起重机两主轴的范数最优迭代学习控制(NOILC)。对于每个轴,NOILC与跟踪误差的线性二次(LQ)状态反馈并行工作。关于重复轨迹的跟踪,ILC部分有助于显著减少从迭代到迭代的跟踪误差,直至由测量信号的质量决定的精度。在本文中,ILC律是基于成本函数的最小化,并涉及前馈和反馈控制动作。该控制结构已在三轴桥式起重机试验台上实现,其中通过CMOS相机确定横向绳索挠度。实验结果表明,该控制结构具有较快的误差收敛速度和较小的剩余跟踪误差。
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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