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Full-Text Articles in Engineering
Theoretical And Experimental Application Of Neural Networks In Spaceflight Control Systems, Pavel Galchenko
Theoretical And Experimental Application Of Neural Networks In Spaceflight Control Systems, Pavel Galchenko
Doctoral Dissertations
“Spaceflight systems can enable advanced mission concepts that can help expand our understanding of the universe. To achieve the objectives of these missions, spaceflight systems typically leverage guidance and control systems to maintain some desired path and/or orientation of their scientific instrumentation. A deep understanding of the natural dynamics of the environment in which these spaceflight systems operate is required to design control systems capable of achieving the desired scientific objectives. However, mitigating strategies are critically important when these dynamics are unknown or poorly understood and/or modelled. This research introduces two neural network methodologies to control the translation and rotation …
Discrete-Time Neural Network Based State Observer With Neural Network Based Control Formulation For A Class Of Systems With Unmatched Uncertainties, Jason Michael Stumfoll
Discrete-Time Neural Network Based State Observer With Neural Network Based Control Formulation For A Class Of Systems With Unmatched Uncertainties, Jason Michael Stumfoll
Masters Theses
"An observer is a dynamic system that estimates the state variables of another system using noisy measurements, either to estimate unmeasurable states, or to improve the accuracy of the state measurements. The Modified State Observer (MSO) is a technique that uses a standard observer structure modified to include a neural network to estimate system states as well as system uncertainty. It has been used in orbit uncertainty estimation and atmospheric reentry uncertainty estimation problems to correctly estimate unmodeled system dynamics. A form of the MSO has been used to control a nonlinear electrohydraulic system with parameter uncertainty using a simplified …