Title :
Stability and convergence of neurologic model based robotic controllers
Author :
Ciliz, M.K. ; Isik, C.
Author_Institution :
Dept. of Electr. Eng., Bosphorous Univ., Istanbul
Abstract :
The authors investigate the local convergence properties of an artificial-neural-network (ANN)-based learning controller, using linearization techniques. The controller utilizes generic multilayer ANNs to adaptively approximate the manipulator dynamics over a specified region of the state space for a given desired trajectory. This generic neural network structure can be viewed as a nonlinear extension of a deterministic autoregressive model which is commonly used in model matching problems for linear systems
Keywords :
convergence; feedforward neural nets; learning (artificial intelligence); linearisation techniques; robots; stability; deterministic autoregressive model; generic neural network; linear systems; linearization; local convergence; manipulator dynamics; model matching; neural net based learning; neurologic model based robotic controllers; stability; state space; Artificial neural networks; Convergence; Linear systems; Linearization techniques; Manipulator dynamics; Multi-layer neural network; Orbital robotics; Robots; Stability; State-space methods;
Conference_Titel :
Robotics and Automation, 1992. Proceedings., 1992 IEEE International Conference on
Conference_Location :
Nice
Print_ISBN :
0-8186-2720-4
DOI :
10.1109/ROBOT.1992.219979
