Adaptive Backstepping Design for a Longitudinal UAV Model Utilizing a Fully Tuned Growing Radial Basis Function Network
نویسندگان
چکیده
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.
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