International Journal of Mechanical Engineering

This study focuses on the research of adaptive grasping control for bionic arms using electromyographic signals. The paper first explains how microelectromyographic signals generated by human muscle movements are acquired through electromyography sensors, followed by signal quality enhancement through preprocessing steps such as filtering and amplification. It then introduces methods for extracting time-domain, frequency-domain, and time-frequency domain features from the processed signals to identify muscle movement intentions. Subsequently, control algorithms are designed based on recognition results, including adaptive control strategies that enable real-time adjustment of control parameters according to object characteristics and environmental changes, achieving stable grasping. The research aims to enhance the accuracy, flexibility, and adaptability of bionic arm grasping, promoting its widespread application in medical rehabilitation, industrial production, and other fields.

Bionic arm; Electromyographic signals; Adaptive control; Grasping control; Signal processing