Title :
Gaussian neural network for direct adaptive control of robotic systems
Author :
Mizerek, Robert T.
Author_Institution :
EG&G Special Projects, Las Vegas, NV, USA
Abstract :
We treat the question of direct adaptive control of multi-link robotic systems with revolute joints. It is assumed that the dynamics of the robotic systems are not known. A radial basis function (RBF) neural network is used in a feedback loop for the control. An adaptive law is derived for the joint angle trajectory tracking. Simulation results are presented to show that in the closed-loop system precise trajectory control is accomplished. Furthermore, the effect of choice of number of neurons in the RBF neural network on the performance of the controller is also examined
Keywords :
adaptive control; closed loop systems; feedforward neural nets; manipulators; neurocontrollers; position control; tracking; Gaussian neural network; closed-loop system; direct adaptive control; feedback loop; joint angle trajectory tracking; multi-link robotic systems; radial basis function neural network; Adaptive control; Control system synthesis; Control systems; Feedback loop; Function approximation; Lyapunov method; Manipulator dynamics; Multi-layer neural network; Neural networks; Robot control;
Conference_Titel :
Decision and Control, 1994., Proceedings of the 33rd IEEE Conference on
Conference_Location :
Lake Buena Vista, FL
Print_ISBN :
0-7803-1968-0
DOI :
10.1109/CDC.1994.411689
