In multi-axis motion control systems, the tracking errors of single axis load and the contour errors caused by the mismatch of dynamic characteristics between the moving axes will affect the accuracy of the motion control system. To solve this issue, a biaxial motion control strategy based on double-iterative learning and cross-coupling control is proposed. The proposed control method improves the accuracy of the motion control system by improving individual axis tracking performance and contour tracking performance. On this basis, a rapid control prototype (RCP) is designed, and the experiment is verified by the hardware and software platforms, LabVIEW and Compact RIO. The whole design shows enhancement in the precision of the motion control of the multiaxis system. The performance in individual axis tracking and contour tracking is greatly improved.
The aim of the studywas to find an effective method of ripple torque compensation for a direct drive with a permanent magnet synchronous motor (PMSM) without time-consuming drive identification. The main objective of the research on the development of a methodology for the proper teaching a neural network was achieved by the use of iterative learning control (ILC), correct estimation of torque and spline interpolation. The paper presents the structure of the drive system and the method of its tuning in order to reduce the torque ripple, which has a significant effect on the uneven speed of the servo drive. The proposed structure of the PMSM in the dq axis is equipped with a neural compensator. The introduced iterative learning control was based on the estimation of the ripple torque and spline interpolation. The structurewas analyzed and verified by simulation and experimental tests. The elaborated structure of the drive system and method of its tuning can be easily used by applying a microprocessor system available now on the market. The proposed control solution can be made without time-consuming drive identification, which can have a great practical advantage. The article presents a new approach to proper neural network training in cooperation with iterative learning for repetitive motion systems without time-consuming identification of the motor.