Modelling and Neuro-Adaptive Robust Control Algorithms for Solid Fuel Rockets

This study presents the development of a methodology for designing neuro-adaptive robust controllers based on a reference model associated with an artificial neural network of radial basis functions (ANN-RBF) for solid fuel suborbital rockets. The modelling and neuro-adaptive robust control algorithms for these rockets are presented. Initially, the methodology is evaluated for a robust controller based on a reference model with ANN-RBF for altitude control. The main objective of the control is to suppress the effect of non-linear uncertainties inherent in the process. The method involves mathematical and computational modelling, together with the design of adaptive controllers for stability and performance analysis. The controllers considered include model reference adaptive control (MRAC) techniques and a model reference neuro-adaptive control (MRNAC) approach. The analysis, carried out using computer simulations, evaluates the behavior of each controller in relation to system stability and performance. The final objective is to select the most suitable controller for the suborbital rocket, taking into account the system constraints, robust performance requirements, robust stability, and optimal adaptability. This research promotes the development of adaptive controllers for suborbital rockets, with possible applications in scientific research and commercial launches.