Robotic Joint Control System based on Analogue Spiking Neural Networks and SMA Actuators

Abstract

The control of human hands and fingers represents one of the most complex functions of the motor cortex. In order to implement anthropomorphic hands that mimic accurately the motion ability of the human hands, the basic biological mechanisms of the natural muscle control should be modelled. This paper presents the design of a significantly improved control system based on analogue neural networks that can be used to replicate the biological control mechanisms of the natural muscles. In order to demonstrate the proposed concept, experiments were performed using a single-joint robotic arm that can be flexed as the human elbow by an artificial muscle connected as the biceps. In order to bring more biological plausibility to the robotic arm, the artificial muscle is implemented using a shape memory alloy wire which actuates by contraction as the natural muscles. Moreover, the contraction force of the actuator wire is directly determined by the spiking frequency of the electronic neurons as the motor neurons determines the contraction strength of the natural muscles.The experimental results show that using excitatory neurons and several inhibitory neurons unevenly distributed on the inputs of the artificial motor neurons that drive the shape memory alloy actuator, the spiking neural network is able to control with high precision the rotation of the arm mobile lever to random target positions even if the arm is slightly loaded.These results validate the control method of the robotic arm junction showing that the analogue spiking neural networks represents a very good alternative to control the contraction of SMA actuators in a biological plausible manner.