基于多分支BP神经网络的气动肌肉迟滞建模方法

doi:10.3969/j.issn.1000-1158.2021.06.09

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Abstract A novel modeling method based on multi-branch BP neural network is proposed to describe the displacement/pressure hysteresis of pneumatic muscle. Firstly, the displacement/pressure hysteresis characteristic test system was built to obtain the displacement/pressure hysteresis loops of pneumatic muscle. Then, the classical BP neural network, multi-branch BP neural network and Prandtl-Ishlinskii model are used to fit the hysteresis loop of pneumatic muscle, respectively. Finally, the comparative study found that the multi-branch BP neural network can effectively avoid the over-fitting phenomenon in the fitting process of classical BP neural network, and the modeling capacity is obviously better than the traditional Prandtl-Ishlinskii model. Compared with Prandtl-Ishlinskii model, the mean average, mean square and maximum errors of multi-branch BP neural network are reduced by 87.45%, 86.68% and 74.73%.

Key words： metrology displacement/pressure hysteresis pneumatic muscle hysteresis modeling multi-branch BP neural network prandtl-ishlinskii model generalization ability

Received: 03 December 2019 Published: 23 June 2021

PACS:	TB92
	TB973

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	Articles by authors
	XIE Sheng-long
	ZHANG Wen-xin
	ZHANG Wei-min
	REN Guo-yin
	LU Yu-jun
	LU Qing

Cite this article:

XIE Sheng-long,ZHANG Wen-xin,ZHANG Wei-min, et al. Hysteresis Modeling Method for Pneumatic Muscle Based on Multi-Branch BP Neural Network[J]. Acta Metrologica Sinica, 2021, 42(6): 745-752.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2021.06.09 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2021/V42/I6/745

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