sEMG based fuzzy control strategy with ANFIS path planning for prosthetic hand

This paper presents an intelligent adaptive neurofuzzy inference system (ANFIS) based fuzzy Mamdani controller for a multifingered prosthetic hand. The objective of the controller is to move the finger joint angles along predetermined paths representing a grasping motion. The initiation of the grasping task is evaluated via EMG-entropy data, measured at the forearm of the prosthetic user. In addition to the motion control, the finger force is regulated with a Fuzzy logic controller. Simulation results indicate good performance of the proposed controller. Results show that the outputs follow the hand/finger force and given reference trajectory closely.