Meade, Andrew J.2021-05-032021-05-032021-052021-04-30May 2021Smith, Graham. "Radial Basis Function Neural Network System Identification and Model Prediction Control for Aircraft." (2021) Master’s Thesis, Rice University. <a href="https://hdl.handle.net/1911/110380">https://hdl.handle.net/1911/110380</a>.https://hdl.handle.net/1911/110380This thesis details efforts made to perform system identification and control of aircraft using a self-assembling radial basis neural network method. First, Sequential Function Approximation (SFAX) is presented as an efficient, accurate and reliable tool for system identification and used to create and train a neural network model of an aircraft’s longitudinal and lateral-directional dynamics, using only input and output data. This is demonstrated on data from a small unconventional UAV design called ICE as well as from the T-38 jet flight test aircraft. Then, this neural network model is applied to a model predictive control scheme using MATLAB optimization routines. Finally, SFAX is used to build a direct inverse neural network controller.application/pdfengCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.Radial Basis FunctionsNeural NetworksModel Predictive ControlAircraft ControlThesis2021-05-03