Data-Driven Based Model-Free Adaptive Optimal Control Method for Hypersonic Morphing Vehicle

Aiming at the attitude control of a class of hypersonic morphing vehicles (HMVs) with variable sweep wings, a model-free adaptive dynamic planning (MFADP) optimal control method based on data-driven and finite-time fuzzy disturbance observer is proposed in this article. An integrated oriented-control attitude-morphing model is established, and morphing is considered as the state to carry out the integrated coupling attitude control. The control scheme is organized by a steady-feedback-compensation framework. To overcome the dependence on the unknown dynamic knowledge of HMVs, the dynamic model is reconstructed using neural networks for steady-state control. Subsequently, based on the MFADP algorithm, an Off–On serial policy learning strategy is designed for the error model to obtain a real-time approximate optimal feedback control. Additionally, a fuzzy disturbance observer with finite-time convergence ability is proposed to estimate and compensate the multiple uncertainties. Finally, the stability of the closed-loop system is proved theoretically and the simulation results demonstrate the improved performance of the proposed method.