Adaptive Critic Attitude Learning Control for Hypersonic Morphing Vehicles Without Backstepping

This article proposes an adaptive critic attitude learning control for hypersonic morphing vehicles under large uncertainties and deformation. First, to remove the recursive design complexity caused by backstepping, an attitude-morphing coupled model in Brunovsky form is created by introducing a coordinate transformation. Second, a disturbance compensation controller is designed without using backstepping, wherein a resource-saving yet efficient unknown system dynamics estimator is introduced to estimate the lumped uncertainties by a simple filtering operation. Then, a near optimal regulator capable of online learning is developed under a critic-only adaptive dynamic programming framework. Notably, an improved finite-time updating law for critic weights is elaborated to achieve assured convergences by extracting weight errors from real-time and historical data. The significant merit is that even with fast morphing and strong uncertainties, good robustness, and optimal performances can be simultaneously attained under a convergence-assured learning setting. Through Lyapunov analysis, the convergences of the tracking error and weight estimation error are proven, guaranteeing the optimality of control strategies. Simulations are offered to demonstrate the advantages and utilities.