The Optimal Adaptive-Based Neurofuzzy Control of the 3-DOF Musculoskeletal System of Human Arm in a 2D Plane.

Amin Valizadeh,Ali Akbar Akbari,Fahd Abd Algalil

doi:10.1155/2021/5514693

Amin Valizadeh, Ali Akbar Akbari + Show 1 more

Open Access

https://doi.org/10.1155/2021/5514693

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Abstract

Each individual performs different daily activities such as reaching and lifting with his hand that shows the important role of robots designed to estimate the position of the objects or the muscle forces. Understanding the body's musculoskeletal system's learning control mechanism can lead us to develop a robust control technique that can be applied to rehabilitation robotics. The musculoskeletal model of the human arm used in this study is a 3-link robot coupled with 6 muscles which a neurofuzzy controller of TSK type along multicritic agents is used for training and learning fuzzy rules. The adaptive critic agents based on reinforcement learning oversees the controller's parameters and avoids overtraining. The simulation results show that in both states of with/without optimization, the controller can well track the desired trajectory smoothly and with acceptable accuracy. The magnitude of forces in the optimized model is significantly lower, implying the controller's correct operation. Also, links take the same trajectory with a lower overall displacement than that of the nonoptimized mode, which is consistent with the hand's natural motion, seeking the most optimum trajectory.

Highlights

In many countries, population aging leads to a decrease in productivity of useful work, and this will cause serious problems
In the robotic human arm, two links are usually used as the arm and forearm segments with two-degree-of-freedom (DOF), and at least four muscle elements are used for moving it in the 2D space
Understanding the training mechanism of the musculoskeletal system of the body can lead us to employ a powerful controller for body rehabilitation robotics

Summary

Introduction

Population aging leads to a decrease in productivity of useful work, and this will cause serious problems. Kambara et al [25] proposed a control model for motion training based on the inverse static model, direct dynamic model, and feedback control combined with Actor-Critic. Their model supported the trajectory prediction of a 2-DOF arm with six artificial muscles. Dong et al [27, 28] implemented an adaptive sliding mode control strategy on a 2-DOF robotic hand with biarticular muscles so that the dynamic parameters were updated, which caused the input disturbances and stimulations of the system to be considered.

The 3-DOF Human Arm Musculoskeletal Model

Controller Design

Results

Conclusion