<p>In this paper, a tracking&nbsp;and synchronizing&nbsp;algorithm to control the dual-drive system&nbsp;is proposed. Recently, it is difficult&nbsp;to see the system&nbsp;in many industries that use only one&nbsp;motor&nbsp;to drive a single axis. Because of the heavy object and transportation&nbsp;in the high velocity, the multi-motors system is necessary.&nbsp;However, this system has the&nbsp;problem with&nbsp;the unsynchronization between&nbsp;motors connected to one axis. This resynchronization makes the system unstable and&nbsp;results in low performance.&nbsp;In this paper, we propose the control algorithm to minimized the tracking error and the synchronizing error. Iterative Learning Control (ILC) algorithm is used to track the system in many industries because it is powerful in the repetitive system. To maximize the convergence speed, the inversion-based ILC algorithm is proposed. By applying&nbsp;an inverse model ILC from modeling through FRF, the system shows the performance to follow the tracking and synchronizing error simultaneously in&nbsp;the dual-drive system. Compared to conventional ILC, this algorithm shows the updating law for the inversion model. This updating makes the system robust in the disturbance and nonlinear components.&nbsp;The constructed algorithm is verified through simulation and experimentation. The proposed algorithm shows a better performance than the conventional control algorithms.</p>

Tracking and Synchronization Control for Dual-Drive System by Using Iterative Learning Control

Paper Proceedings: https://asmedigitalcollection.asme.org/IDETC-CIE/IDETC-CIE2021/volume/85369

ASME 2021 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC/CIE2021)

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