Title :
Adaptive output tracking of partly known robotic systems using SoftMax function networks
Author :
Kumarawadu, Sisil ; Watanabe, Keigo ; Kiguchi, Kazuo ; Izumi, Kiyotaka
Author_Institution :
Dept. of Adv. Syst. Control Eng., Saga Univ., Japan
fDate :
6/24/1905 12:00:00 AM
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
Keywords :
Gaussian processes; active vision; adaptive control; neurocontrollers; optical tracking; robot vision; velocity control; Gaussian functions; SoftMax function networks; adaptive control; binocular robotic head; neural gas algorithm; neural network model; output-tracking; resolved velocity control; robot vision; Adaptive control; Artificial neural networks; Control engineering; Control systems; Neck; Network topology; Neural networks; Nonlinear control systems; Programmable control; Robots;
Conference_Titel :
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7278-6
DOI :
10.1109/IJCNN.2002.1005520