Force Control for Anthropomorphic Fingers Actuated by Shape Memory Alloy Wires

Abstract

High accuracy in modelling the behavior of human hand and fingers is obtained using control devices of high biological plausibility. Such devices are typically based on neural networks and are able to control in parallel multiple artificial muscles. This paper presents the structure of an electronic spiking neural network that was implemented to control the force of two opposing fingers of an anthropomorphic hand. In order to increase the level of bio-inspiration, the artificial muscles are implemented using shape memory alloy wires which actuates by contraction as the natural muscles. Moreover, the contraction force of the SMA actuators is directly related to the spiking frequency that is generated by the artificial neurons. The results show that using few excitatory and inhibitory neurons the neural network is able to set and regulate the contraction force of the SMA actuators.