High precision control of ball screw driven stage using repetitive control with sharp roll-off learning filter

Takemura, Tadashi; Fujimoto, Hiroshi

doi:10.1109/amc.2012.6197039

Cited by 1 publication

(2 citation statements)

References 8 publications

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“…Compared with (18) and (24), the first and second terms on the right-hand side are identical. To guarantee the error convergence stability [see (15)], Q is generally designed with a small gain in the high-frequency band where |SΔ| has a high gain, as shown in Fig. 14.…”

Section: A N-times Learning Filtermentioning

confidence: 99%

“…This technique is widely used for rejecting periodic disturbances or for tracking a periodic reference signal [11], [12]. As one of the RC techniques, repetitive perfect tracking control (RPTC) has been proposed in [13], and it achieved high tracking performance with a ball-screw-driven stage [14], [15]. From the standpoint of high-speed positioning, a small number of learning iterations are required.…”

Section: Introductionmentioning

confidence: 99%

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High-Precision Control of Ball-Screw-Driven Stage Based on Repetitive Control Using $n$-Times Learning Filter

Fujimoto

Takemura

2014

IEEE Trans. Ind. Electron.

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This paper presents a novel learning control method for ball-screw-driven stages. In recent years, many types of friction models that are based on complicated equations have been studied. However, it is difficult to treat friction models with equations because the level of precision that is associated with real friction characteristics and parameter tuning are difficult to achieve. In contrast, repetitive perfect tracking control (RPTC) is a repetitive control technique that achieves high-precision positioning. In this paper, we propose the use of RPTC with n-times learning filter. The n-times learning filter has a sharper rolloff property than conventional learning filters. With the use of the n-times learning filter, the proposed RPTC can converge tracking errors n times faster than the RPTC with the conventional learning filter. Simulations and experiments with a ball-screw-driven stage show the fast convergence of the proposed RPTC. Finally, the proposed learning control scheme is combined with data-based friction compensation, and the effectiveness of this combination is verified for the x-y stage of a numerically controlled machine tool.Index Terms-n-times learning, perfect tracking control, repetitive control, zero-phase low-phase filter.

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Section: A N-times Learning Filtermentioning

confidence: 99%