| Popis: |
Classical Radial Basis Function (RBF) neural network controller designs typically fix the number of basis functions and tune only the weights. In this paper we present a backstepping neural network controller algorithm in which all RBF parameters, including centers, variances and weight matrices are tuned online. By using a Lyapunov approach, tuning rules for updating the RBF parameters are derived and a stability and robustness analysis is presented. Additionally, we incorporate the ability to append RBF neurons such that both tracking performance and computational cost can be optimized. The condition for adding a neuron is based on a sliding RMS error window. In addition to the theoretical results, we present the controller implemented in several simulations of an auto landing sequence for a unmanned aerial vehicle (UAV) model. |
