Adaptive control of hypersonic vehicles based on characteristic models with fuzzy neural network estimators

Strong uncertainties and time-variations of hypersonic vehicles during the reentry phase pose huge challenges to their control system design. This paper addresses a novel adaptive output feedback control scheme based on characteristic models with fuzzy neural network estimators to guarantee the stable and accurate attitude tracking for the hypersonic vehicle, which is subject to unknown time-varying aerodynamics. By characteristic modeling, the time-varying uncertainties are integrated into several characteristic parameters to be estimated online, which inherit the time-variation property from the aerodynamics. And then the characteristic model-based adaptive control law is constructed, while the fuzzy neural network estimators are designed to estimate the time-varying characteristic parameters. The control performances including the property of estimators and the attitude tracking error are also analyzed. At last, the effectiveness of the proposed adaptive control scheme is illustrated by several representative simulations.