Industrial servo systems usually have high gear ratio reducers, which introduce flexibility and transmission errors. The consequential vibrations and compliance bring difficulties to many demanding applications. For applications in which the servos move repetitively, iterative learning control (ILC) is a powerful tool to improve performance. An intuitive implementation of ILC for flexibility compensation involves combining a torque ILC with a motor reference ILC. This paper explains why such a direct combination does not work well. A systematic synthesis method is introduced to address the coupling between torque learning and reference learning. In addition, a robust control method is proposed to address system uncertainties. The proposed method is demonstrated using a servo control example.
- Dynamic Systems and Control Division
Compensating Flexibility in Servo Systems Using Iterative Learning Control
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Wang, C, Wang, Z, Peng, C, Zhao, Y, & Tomizuka, M. "Compensating Flexibility in Servo Systems Using Iterative Learning Control." Proceedings of the ASME 2015 Dynamic Systems and Control Conference. Volume 3: Multiagent Network Systems; Natural Gas and Heat Exchangers; Path Planning and Motion Control; Powertrain Systems; Rehab Robotics; Robot Manipulators; Rollover Prevention (AVS); Sensors and Actuators; Time Delay Systems; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamics Control; Vibration and Control of Smart Structures/Mech Systems; Vibration Issues in Mechanical Systems. Columbus, Ohio, USA. October 28–30, 2015. V003T40A001. ASME. https://doi.org/10.1115/DSCC2015-9659
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