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

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.

Gait multi-objectives optimization of lower limb exoskeleton robot based on BSO-EOLLFF algorithm

“…It can be seen from Fig. 7 that the traditional PSO algorithm stops searching after 40 iterations and falls into the local optimum due to its weak global search ability [21]. When the BSO algorithm iterates 23 times, the function fitness value tends to be stable.…”

“…Based on the beetle antennae search algorithm and particle swarm optimization (PSO) algorithm, the BSO algorithm was designed [21,22]. The BSO algorithm has the characteristics of fast solving speed and high accuracy and has been successfully applied in the fields of signal positioning [23] and data classification [24].…”

Autonomous motion and control of lower limb exoskeleton rehabilitation robot

“…Control system uncertainty and external perturbations are the main causes of control system instability. Several algorithms such as neural networks (Yang and Gao, 2019), machine learning (Sun et al, 2022), adaptive control (Wu et al, 2018;Sun et al, 2021), sliding mode control (Kong et al, 2019), and intelligent swarms, are mainly used to solve these problems (Soleimani Amiri et al, 2020;Guo et al, 2022;Zhang and Zhang, 2022).…”

Adaptive Density Spatial Clustering Method Fusing Chameleon Swarm Algorithm

“…Since the swarm intelligence algorithm has a good global search capability, many scholars use this method to adaptively search for the parameters of the density clustering algorithm [25][26][27][28]. Zhu et al [29] proposed the K-DBSCAN algorithm.…”