Function-Level Adaptive Model Predictive Control Applied to Attitude Tracking of High-Speed Aircraft

External disturbances pose significant challenges to the attitude tracking and precision maintenance of high-speed flight vehicles. To enhance the tracking accuracy, this paper proposes a function-level adaptive model predictive control (FAMPC) approach. In contrast to conventional parameter-adaptive methods, the proposed functional adaptive law (FAL) estimates unknown disturbances directly without requiring auxiliary parameterized approximators. Furthermore, rather than relying on terminal invariant sets for stability guarantees, a Lyapunov-based model predictive control (MPC) framework is formulated. The core approach involves synthesizing an auxiliary controller, whose influence is incorporated as an additional constraint to enforce a specific decay rate for a properly selected Lyapunov function, thereby ensuring closed-loop stability and recursive feasibility. Rigorous theoretical analysis formally establishes the recursive feasibility and closed-loop stability under the proposed Lyapunov-based framework, while extensive simulation studies validate the effectiveness of the proposed control strategy.