Stability and convergence of neurologic model based robotic controllers
M. Kemal Cılız, C. Isik · 2003
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.>