Abstract

Most robots that are actuated by antagonistic pneumatic artificial muscles are controlled by various control algorithms that cannot adequately imitate the actual muscle distribution of human limbs. Other robots in which the distribution of pneumatic artificial muscle is similar to that of human limbs can only analyze the position of the robot using perceptual data instead of rational knowledge. In order to better imitate the movement of a human limb, the article proposes a humanoid lower limb in the form of a parallel mechanism where muscle is unevenly distributed. Next, the kinematic and dynamic movements of bionic hip joint are analyzed, where the joint movement is controlled by an observer-based fuzzy adaptive control algorithm as a whole rather than each individual pneumatic artificial muscle and parameters that are optimized by a neural network. Finally, experimental results are provided to confirm the effectiveness of the proposed method. We also document the role of muscle in trajectory tracking for the piriformis and musculi obturator internus in isobaric processes.

Highlights

  • Pneumatic artificial muscle (PAM) comprises an internal rubber hose, external fiber, and a metallic sheath fixed at both ends

  • Other researchers who studied on control algorithms have used a sliding mode control based on a nonlinear disturbance observer,[5] an adaptive impedance controller based on an evolutionary dynamic fuzzy neural network,[6] and fractional fuzzy adaptive sliding mode control.[7]

  • After analyzing the biological muscle structure of the human lower limb, we propose a humanoid lower limb actuated by PAM in the form of a parallel mechanism where the muscle is unevenly distributed instead of employing antagonistic muscles

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Summary

Introduction

Pneumatic artificial muscle (PAM) comprises an internal rubber hose, external fiber, and a metallic sheath fixed at both ends. Keywords Humanoid robot, parallel manipulator, fuzzy adaptive control, neural network, parameter optimization

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