Title :
Robot tracking in task space using neural networks
Author :
Feng, Gang ; Chak, C.K.
Author_Institution :
Sch. of Electr. Eng., New South Wales Univ., Kensington, NSW, Australia
fDate :
27 Jun-2 Jul 1994
Abstract :
This paper considers tracking control of robots in task space. A new control scheme is proposed based on a kind of conventional controller and a neural network based compensating controller. This scheme takes advantages of simplicity of the model based control approach and uses the neural network controller to compensate for the robot modelling uncertainties. The neural network is trained online based on Lyapunov theory and thus its convergence is guaranteed
Keywords :
Lyapunov methods; convergence; learning (artificial intelligence); neural nets; neurocontrollers; real-time systems; robots; tracking; Lyapunov theory; compensating controller; convergence; model based control; neural networks; online learning; robots; task space; tracking control; Adaptive control; Artificial neural networks; Equations; Feedforward neural networks; Intelligent networks; Manipulator dynamics; Neural networks; Orbital robotics; Robot control; Robot kinematics;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.374684
