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 (RVC) technique and a neurally-inspired adaptive compensating paradigm constructed using SoftMax function networks and Neural Gas (NG) algorithm. Results of simulations on our active binocular head are reported. The neural network (NN) model is constructed to have two neural subnets to separately take care of robot head's neck and eye control simplifying the design and making for faster weight tuning algorithms.
|出版ステータス||Published - 1月 1 2002|
|イベント||2002 International Joint Conference on Neural Networks (IJCNN '02) - Honolulu, HI|
継続期間: 5月 12 2002 → 5月 17 2002
|Other||2002 International Joint Conference on Neural Networks (IJCNN '02)|
|Period||5/12/02 → 5/17/02|
ASJC Scopus subject areas