Adaptive output tracking of partly known robotic systems using SoftMax function networks

Abstract

In this paper, a neural-network-based adaptive control scheme is presented to solve the output-tracking problem of a robotic system with unknown nonlinearities. The control scheme ingeniously combines the conventional resolved velocity control technique and a neurally-inspired adaptive compensating paradigm constructed using SoftMax function networks and neural gas algorithm. Results of simulations on our active binocular head are reported. The neural network model constructed to has two neural subnets to separately control the robot head neck and eye movement, simplifying the design and leading to faster weight tuning algorithms.