基于自适应粒子群遗传算法的柔性关节机器人动力学参数辨识

doi:10.3969/j.issn.1000-1158.2020.01.12

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Abstract A method of dynamic parameter identification of flexible joint robot based on adaptive particle swarm genetic algorithm was proposed. The algorithm adopted dynamic adaptive adjustment strategy to improve the convergence speed of particle swarm algorithm. At the same time, a new hybrid genetic algorithm was introduced to avoid particle swarm optimization. Adaptive particle swarm genetic algorithm was compared with the standard particle swarm algorithm, genetic algorithm and artificial swarm algorithm, and the simulation results showed that the algorithm performs parameter identification after about 60 iterations, and the relative error of each parameter was reduced to less than 1%. Finally, the experimental verification was carried out by using the rotating flexible joint experimental platform, and the experimental results showed that the algorithm has better convergence speed and optimization precision.

Key words： metrology dynamic paramete parameter identification adaptive hybrid algorithm particle swarm optimization genetic algorithm flexible joint robot

Received: 09 April 2018 Published: 19 December 2019

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TB93

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	Articles by authors
	WANG Yue-ling
	WANG Yue
	WANG Qi
	WANG Hong-bin

Cite this article:

WANG Yue-ling,WANG Yue,WANG Qi, et al. Dynamic Parameter Identification of Flexible Joint Robot Based on Adaptive Particle Swarm Optimization-genetic Algorithm[J]. Acta Metrologica Sinica, 2020, 41(1): 60-66.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2020.01.12 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2020/V41/I1/60

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