Adaptive Prescribed Performance Control for HFVs With AoA Constraint and Input Saturation

Designing a controller for HFVs with multiple constraints is challenging due to conflicts among the input saturation, the asymmetric angle of attack (AoA) constraint, and the prescribed tracking performance (PTP) constraint. To address this issue, this paper develops a radial basis function neural networks (RBF NNs)-based controller that maintains the input saturation, the asymmetric AoA constraint, and the PTP constraint when no conflicts arise and degrades the PTP constraint to an adaptive prescribed tracking performance (APTP) constraint when conflicts occur. The input saturation is handled by leveraging RBF NNs adaptive control architecture. The asymmetric AoA constraint is enforced using an explicit safety filter based on a symmetric barrier Lyapunov function. The APTP constraint is ensured through an auxiliary system activated adaptively by the input saturation and AoA constraint. The proposed controller architecture effectively achieves the specified control objectives despite the system uncertainties, demonstrating its performance through numerical simulations.