Premium
Robust model predictive control of a micro machine tool for tracking a periodic force signal
Author(s) -
Wang Yuzhang,
Geng Yanquan,
Yan Yongda,
Wang Jiqiang,
Fang Zhuo
Publication year - 2020
Publication title -
optimal control applications and methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.458
H-Index - 44
eISSN - 1099-1514
pISSN - 0143-2087
DOI - 10.1002/oca.2642
Subject(s) - control theory (sociology) , model predictive control , signal (programming language) , actuator , machining , voltage , controller (irrigation) , displacement (psychology) , machine tool , piezoelectricity , computer science , trajectory , tracking (education) , engineering , mechanical engineering , control (management) , artificial intelligence , physics , psychology , agronomy , pedagogy , electrical engineering , astronomy , psychotherapist , biology , programming language
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.