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Lower limb exoskeleton robots’ dynamics parameters identification based on improved beetle swarm optimization algorithm

Published online by Cambridge University Press:  07 January 2022

Peng Zhang  and
Junxia Zhang Open the ORCID record for Junxia Zhang [Opens in a new window]
Peng Zhang
Affiliation:
Tianjin University of Science and Technology, Tianjin 300222, China Tianjin Key Laboratory of Integrated Design and Online Monitoring of Light Industry & Food Engineering Machinery and Equipment, Tianjin 300222, China
Junxia Zhang*
Affiliation:
Tianjin University of Science and Technology, Tianjin 300222, China Tianjin Key Laboratory of Integrated Design and Online Monitoring of Light Industry & Food Engineering Machinery and Equipment, Tianjin 300222, China
*
*Corresponding author: Junxia Zhang. E-mail: zjx@tust.edu.cn
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Abstract

Efficient and high-precision identification of dynamic parameters is the basis of model-based robot control. Firstly, this paper designed the structure and control system of the developed lower extremity exoskeleton robot. The dynamics modeling of the exoskeleton robot is performed. The minimum parameter set of the identified parameters is determined. The dynamic model is linearized based on the parallel axis theory. Based on the beetle antennae search algorithm (BAS) and particle swarm optimization (PSO), the beetle swarm optimization algorithm (BSO) was designed and applied to the identification of dynamic parameters. The update rule of each particle originates from BAS, and there is an individual’s judgment on the environment space in each iteration. This method does not rely on the historical best solution in the PSO and the current global optimal solution of the individual particle, thereby reducing the number of iterations and improving the search speed and accuracy. Four groups of test functions with different characteristics were used to verify the performance of the proposed algorithm. Experimental results show that the BSO algorithm has a good balance between exploration and exploitation capabilities to promote the beetle to move to the global optimum. Besides, the test was carried out on the exoskeleton dynamics model. This method can obtain independent dynamic parameters and achieve ideal identification accuracy. The prediction result of torque based on the identification method is in good agreement with the ideal torque of the robot control.

Keywords

parameters identificationexoskeleton robotbeetle swarm optimization algorithm
Type
Research Article
Information
Robotica , Volume 40 , Issue 8 , August 2022 , pp. 2716 - 2731
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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