Fuzzy Approximation-Based Fixed-Time Attitude Control for a Hypersonic Reentry Vehicle With Full-State Constraints

In this research, we introduce a fuzzy approximation-based control method for an uncertain hypersonic reentry vehicle (HRV) system, which faces full-state constraints encompassing both attitude angles and angular velocities. To safeguard against any breach of full-state constraints, an asymmetric barrier Lyapunov function (ABLF) is employed. This ABLF is notably versatile, capable of accommodating the HRV system under symmetric/asymmetric constraints or even in the absence of any constraint. Furthermore, we separately address the model uncertainties and disturbance unknowns within the system. The introduction of a fuzzy neural network (FNN) algorithm facilitates the approximation of the uncertain model, while a specifically designed disturbance observer aims to pinpoint unknown disturbances. Additionally, the control scheme not only effectively manages the complexities inherent in attitude control but also ensures the fixed-time convergence for the HRV system. This is crucial for ensuring the HRV system's mission success, including critical phases such as reentry and landing. To substantiate the efficacy of our proposed control scheme, an extensive array of simulations is provided, illustrating its practicality and effectiveness.