Resilient Composite Learning Neuro-Adaptive Integrated Guidance and Control for reusable Launch Vehicle

Abstract

An integrated guidance and control system for tracking a reference flight path trajectory of a reusable launch vehicle in the presence of model uncertainties, external disturbances, and measurement noises is proposed. To achieve this goal, a properly-trained feedforward neural network to estimate uncertainties in conjunction with a disturbance observer, which estimates external disturbances and the estimation error made by the neural network, is implemented in both the guidance and control loops. In addition, a state observer is designed whose state estimation error is included (along with the tracking error) in the learning algorithm of the neural network and the disturbance observer; such learning is called composite learning. The combined neural network and disturbance observer (employed in both the guidance and control loops) can consistently compensate for complex uncertain terms in the dynamic model of a reusable launch vehicle. More specifically, the introduced design results in an asymptotic tracking of the reference command, thereby providing a resilient flight control system. Extensive simulations are performed to show the effectiveness and robustness of the proposed integrated guidance and control system for trajectory tracking in the presence of uncertainties, aerodynamic disturbances, and measurement noises.