High-performance robust motion control of machine tools: an adaptive robust control approach and comparative experiments

TLDR

The study investigates high‑performance robust motion control of machine tools. The authors propose an adaptive robust control (ARC) scheme that replaces the traditional disturbance observer, endowing the closed‑loop system with robustness to model uncertainties, large parameter variations, and control saturation through built‑in anti‑integration windup, while allowing additional nonlinear robust terms and parameter adaptations. Experimental comparisons demonstrate that ARC outperforms DOB in tracking accuracy and transient response under discontinuous disturbances, reduces reliance on costly friction identification, and achieves lower‑order, higher‑performance control.

Abstract

This paper studies the high-performance robust motion control of machine tools. The newly proposed adaptive robust control (ARC) is applied to make the resulting closed-loop system robust to model uncertainties, instead of the disturbance observer (DOB) design previously tested by many researchers. Compared to DOB, the proposed ARC has a better tracking performance and transient in the presence of discontinuous disturbances, such as Coulomb friction, and it is of a lower order. As a result, time-consuming and costly rigorous friction identification and compensation is alleviated, and overall tracking performance is improved. The ARC design can also handle large parameter variations and is flexible in introducing extra nonlinear robust control terms and parameter adaptations to further improve the transient response and tracking performance. An anti-integration windup mechanism is inherently built in the ARC and, thus, the problem of control saturation is alleviated. Extensive comparative experimental tests are performed, and the results show the improved performance of the proposed ARC.