RBFNN-Based Anti-Input Saturation Control for Hypersonic Vehicles
Bangchu Zhang, Yiyong Liang, Shuitao Rao, Yu Kuang, Weiyu Zhu
Abstract
In hypersonic flight control, characterized by challenges posed by input saturation, model parameter uncertainties, and external disturbances, this paper introduces a pioneering anti-input saturation control method based on RBFNN adaptivity. We have developed adaptive laws to enhance control system adaptability and robustness by integrating mission profiles, actuator saturation failure modes, and self-evolving neural network design. Furthermore, our approach introduces a novel anti-input saturation auxiliary system, effectively addressing input saturation constraints. This innovation ensures system stability and precise tracking, even in severe input saturation constraints. The results reveal that the system’s steady-state tracking error remains under 2% under input saturation constraints, and the convergence speed demonstrates an impressive 20% improvement. These findings underscore this research’s substantial advancement in hypersonic flight control. It may significantly enhance the controllability and performance of hypersonic vehicles in real-world scenarios.