Article ID Journal Published Year Pages File Type
1548501 Progress in Natural Science: Materials International 2009 9 Pages PDF
Abstract

In this paper, we investigate deterministic learning in environments with disturbances. We will show that for a class of uncertain nonlinear systems with bounded disturbances, by using an appropriately designed adaptive neural controller, the disturbances are attenuated and the system output tracks a periodic orbit in finite time. As radial basis function (RBF) neural networks (NN) are employed, this leads to the satisfaction of a partial persistence of the excitation (PE) condition. By using the uniform complete observability (UCO) technique, it is analyzed that partial estimated NN weights will converge to a neighborhood of zero, with the size of the neighborhood depending on the amplitude of disturbances as well as on the control gains. Locally-accurate approximation of unknown system dynamics can still be achieved in the stable NN control process. The approximation error level is influenced by the amplitude of disturbances. The obtained knowledge of system dynamics can be reused in another control process towards stability and improved performance. Simulation studies are included to demonstrate the effectiveness of the approach.

Related Topics
Physical Sciences and Engineering Materials Science Electronic, Optical and Magnetic Materials
Authors
, ,