Stochastic Model Predictive Control for Flight Path Angle Tracking of F-16 Aircraft based on Polynomial Chaos

Developing a reliable control system to manage Flight Path Angle (FPA) in the face of parameter uncertainties presents a challenge in F-16 Aircraft operations. This paper presents a novel approach, Stochastic Model Predictive Control (SMPC), utilized for FPA tracking amid probabilistic time-invariant parameters using Polynomial Chaos Expansion (PCE). A numerical case study employing longitudinal motion with uncertain parameters in aerodynamic coefficients within the system matrix validates the proposed method. Simulation results indicate that this approach exhibits greater robustness than nominal Model Predictive Control (MPC), with system outputs consistently aligning with the desired flight path angle value across all parameter realizations.