Robust model predictive control of a micro machine tool for tracking a periodic force signal. (16th July 2020)

• Record Type: Journal Article
• Authors: Wang, Yuzhang; Geng, Yanquan; Yan, Yongda; Wang, Jiqiang; Fang, Zhuo
• Other Names: Yuan Shuai guestEditor.; Wan Yimin guestEditor.; Zhang Lixian guestEditor.; Baldi Simone guestEditor.
• Is Part Of: Optimal control applications and methods. Volume 41:Number 6(2020)
• Page Start: 2037
• Page End: 2047
• Publication Date: 2020-07-16
• Subjects: force modulation -- micro machine tool -- periodic force signal tracking -- robust model predictive control; Control theory -- Periodicals; Mathematical optimization -- Periodicals; 629.8312
• DOI: 10.1002/oca.2642
• Languages: English
• ISSNs: 0143-2087

Abstract

Summary: The micro machine tool that can produce nanostructures by force modulation approach plays a significant role in nanotechnology. In this paper, to guarantee fast and high‐precision cutting subject to external disturbances and input saturation, a robust model predictive control (MPC) using a tube‐based method is exploited to develop a controller for the machining system consisting of a piezoelectric tube (PZT) actuator, a force sensor and a cutting tool, which updates the state of the art. In particular, the dynamic model of the machining system, with the voltage fed into PZT being input and the cutting force being output, is identified by incorporating the map between the cutting force and the displacement of PZT. Based on the voltage‐force dynamic model, a tube‐based MPC controller that consists of two optimizers is used to make PZT actuator track a desired periodic force signal. Finally, the effectiveness of the MPC method for force signal tracking under different frequencies is validated and advantages over the conventional proportional integral controller are also shown in the presence of the constraints of saturated input and external disturbances via numerical simulations.