Adaptive feed-forward friction compensation through developing an asymmetrical dynamic friction model. (April 2022)
- Record Type:
- Journal Article
- Title:
- Adaptive feed-forward friction compensation through developing an asymmetrical dynamic friction model. (April 2022)
- Main Title:
- Adaptive feed-forward friction compensation through developing an asymmetrical dynamic friction model
- Authors:
- Wan, Min
Dai, Jia
Zhang, Wei-Hong
Xiao, Qun-Bao
Qin, Xue-Bin - Abstract:
- Abstract: This article presents an adaptive feed-forward method to compensate for the dynamic friction in the machine tool systems so that the tracking errors of the machine axes can be reduced. To achieve this aim, a dynamic friction model is firstly established by constructing a continuous and differentiable two-segmented arc tangent curve to characterize the asymmetrical and nonlinear static friction phenomenon in relation to the positive and negative velocity ranges of the machine axes. Subsequently, a new feed-forward method is proposed to compensate for the friction by constructing an adaptive controller with three advantages, i.e. no need of velocity and acceleration measurement, large adaptive rate and high steady-state accuracy. During the model establishment, a discontinuous projection mapping in the learning process is constructed. Especially, the stability of the adaptive controller is strictly proved by theoretical derivation. Besides, a new two-step adaptive method is formulated to analytically calculate the unobservable parameter z . Finally, both simulations and experiments confirm that the proposed compensation method can reach to lower tracking errors, especially in the velocity reversal area, by comparing with the existing approaches. Highlights: A new feed-forward method for friction compensation is proposed. An asymmetrical and nonlinear dynamic friction model is established. An adaptive controller is constructed and theoretically proved. An adaptiveAbstract: This article presents an adaptive feed-forward method to compensate for the dynamic friction in the machine tool systems so that the tracking errors of the machine axes can be reduced. To achieve this aim, a dynamic friction model is firstly established by constructing a continuous and differentiable two-segmented arc tangent curve to characterize the asymmetrical and nonlinear static friction phenomenon in relation to the positive and negative velocity ranges of the machine axes. Subsequently, a new feed-forward method is proposed to compensate for the friction by constructing an adaptive controller with three advantages, i.e. no need of velocity and acceleration measurement, large adaptive rate and high steady-state accuracy. During the model establishment, a discontinuous projection mapping in the learning process is constructed. Especially, the stability of the adaptive controller is strictly proved by theoretical derivation. Besides, a new two-step adaptive method is formulated to analytically calculate the unobservable parameter z . Finally, both simulations and experiments confirm that the proposed compensation method can reach to lower tracking errors, especially in the velocity reversal area, by comparing with the existing approaches. Highlights: A new feed-forward method for friction compensation is proposed. An asymmetrical and nonlinear dynamic friction model is established. An adaptive controller is constructed and theoretically proved. An adaptive method is formulated to calculate the unobservable parameter z . The method can achieve quick transient response and high steady state accuracy. … (more)
- Is Part Of:
- Mechanism and machine theory. Volume 170(2022)
- Journal:
- Mechanism and machine theory
- Issue:
- Volume 170(2022)
- Issue Display:
- Volume 170, Issue 2022 (2022)
- Year:
- 2022
- Volume:
- 170
- Issue:
- 2022
- Issue Sort Value:
- 2022-0170-2022-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-04
- Subjects:
- Feed-forward friction compensation -- Adaptive controller -- Asymmetrical dynamic friction -- Tracking errors
Machine theory -- Periodicals
Machinery -- Periodicals
Machines -- Périodiques
Génie mécanique -- Périodiques
Machine theory
Machinery
Periodicals
621.81 - Journal URLs:
- http://www.sciencedirect.com/science/journal/0094114X ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.mechmachtheory.2021.104691 ↗
- Languages:
- English
- ISSNs:
- 0094-114X
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 5424.570800
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 20687.xml