Research on Lower Limb Exoskeleton Trajectory Tracking Control Based on the Dung Beetle Optimizer and Feedforward Proportional–Integral–Derivative Controller

The lower limb exoskeleton (LLE) plays an important role in production activities requiring assistance and load bearing. One of the challenges is to propose a control strategy that can meet the requirements of LLE trajectory tracking in different scenes. Therefore, this study proposes a control strategy (DBO–FPID) that combines the dung beetle optimizer (DBO) with feedforward proportional–integral–derivative controller (FPID) to improve the performance of LLE trajectory tracking in different scenes. The Lagrange method is used to establish the dynamic model of the LLE rod, and it is combined with the dynamic equations of the motor to obtain the LLE transfer function model. Based on the LLE model and target trajectory compensation, the feedforward controller is designed to achieve trajectory tracking in different scenes. To obtain the best performance of the controller, the DBO is utilized to perform offline parameter tuning of the feedforward controller and PID controller. The proposed control strategy is compared with the DBO tuning PID (DBO–PID), particle swarm optimizer (PSO) tuning FPID (PSO–FPID), and PSO tuning PID (PSO–PID) in simulation and joint module experiments. The results show that DBO–FPID has the best accuracy and robustness in trajectory tracking in different scenes, which has the smallest sum of absolute error (IAE), mean absolute error (MEAE), maximum absolute error (MAE), and root mean square error (RMSE). In addition, the MEAE of DBO–FPID is lower than 1.5 degrees in unloaded tests and lower than 3.6 degrees in the hip load tests, with only a few iterations, showing great practical potential.

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Publication Details

Journal
Actuators
Published
2024-09-06
DOI
https://doi.org/10.3390/act13090344
Citations
2
Primary Topic
Prosthetics and Rehabilitation Robotics
Type
article
Field-Weighted Citation Impact
0.37
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article

Research on Lower Limb Exoskeleton Trajectory Tracking Control Based on the Dung Beetle Optimizer and Feedforward Proportional–Integral–Derivative Controller

Haiting Di, Ke Liu, Chang Ming Li, Yongwang Liu
2 citations
Actuators
Prosthetics and Rehabilitation Robotics
0.37
article

Research on Lower Limb Exoskeleton Trajectory Tracking Control Based on the Dung Beetle Optimizer and Feedforward Proportional–Integral–Derivative Controller

Haiting Di, Ke Liu, Chang Ming Li, Yongwang Liu
article en
2 citations

Abstract

The lower limb exoskeleton (LLE) plays an important role in production activities requiring assistance and load bearing. One of the challenges is to propose a control strategy that can meet the requirements of LLE trajectory tracking in different scenes. Therefore, this study proposes a control strategy (DBO–FPID) that combines the dung beetle optimizer (DBO) with feedforward proportional–integral–derivative controller (FPID) to improve the performance of LLE trajectory tracking in different scenes. The Lagrange method is used to establish the dynamic model of the LLE rod, and it is combined with the dynamic equations of the motor to obtain the LLE transfer function model. Based on the LLE model and target trajectory compensation, the feedforward controller is designed to achieve trajectory tracking in different scenes. To obtain the best performance of the controller, the DBO is utilized to perform offline parameter tuning of the feedforward controller and PID controller. The proposed control strategy is compared with the DBO tuning PID (DBO–PID), particle swarm optimizer (PSO) tuning FPID (PSO–FPID), and PSO tuning PID (PSO–PID) in simulation and joint module experiments. The results show that DBO–FPID has the best accuracy and robustness in trajectory tracking in different scenes, which has the smallest sum of absolute error (IAE), mean absolute error (MEAE), maximum absolute error (MAE), and root mean square error (RMSE). In addition, the MEAE of DBO–FPID is lower than 1.5 degrees in unloaded tests and lower than 3.6 degrees in the hip load tests, with only a few iterations, showing great practical potential.

ActuatorsVol. 13(9)
Northeast Forestry University (CN)
Openalex Percentile: Top 44%
Prosthetics and Rehabilitation Robotics
0.37
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