This paper presents a neural adaptive flight control for longitudinal dynamics of air-breathing hypersonic vehicles (AHVs) with constrained aerodynamic surfaces. Multiple actuator constraints including magnitude, rate, and first-order dynamic model in both the elevator and canard are transformed into a specific control allocation problem, which can be readily solved using the standard model predictive control (MPC) technique. Furthermore, an adaptive control scheme is developed combining with the above control allocation and the recurrent cerebellar model articulation controller (RCMAC), which well handles actuator constraints and uncertain factors including aerodynamic coefficients, external disturbances, and flexible dynamics. Numerous simulation results verify performance and robustness of the proposed neural adaptive control.

本文提出了一种用于具有受限气动表面的吸气式高超声速飞行器 (AHV) 纵向动力学的神经自适应飞行控制。电梯和鸭翼中的多个执行器约束（包括幅度、速率和一阶动态模型）被转换为特定的控制分配问题，可以使用标准模型预测控制 (MPC) 技术轻松解决。此外，结合上述控制分配和循环小脑模型关节控制器（RCMAC），开发了一种自适应控制方案，可以很好地处理执行器约束和不确定因素，包括气动系数、外部干扰和灵活动力学。大量仿真结果验证了所提出的神经自适应控制的性能和鲁棒性。