Аннотация
Recent developments in aerospace control systems emphasize the need for intelligent and resilient algorithms capable of handling the complex nonlinearities and uncertainties of spacecraft dynamics. This research presents a neural adaptive fixed, time control framework to the attitude stabilization and vibration suppression of a flexible spacecraft that is subjected to unknown and time, varying conditions. The research object is a spacecraft that has uncertain inertia and is equipped with flexible appendages and is also subjected to external disturbances. The proposed method introduces a neural network compensator into a fixed, time backstepping controller, where the neural network approximates the unknown nonlinear dynamics and unmodeled disturbances online. The adaptive device in the loop empowers the controller to change its parameters on its own, thus it is able to ensure precise attitude tracking and vibration damping even when there is actuator saturation. Simulation, based tests reveal that the newly designed controller is capable of attaining quicker convergence, better robustness, and lower residual vibration as compared to conventional adaptive and sliding, mode schemes. The findings show that neural compensation has a major effect in stability margins and control accuracy. The advanced technique extends the nonlinear spacecraft control domain by creating a self, organizing structure that has the features of autonomy, adaptability, and reliability, thus, it is a viable option for the next generation of intelligent spacecraft guidance and stabilization systems