Sequential Data-Assisted Control in Flight

Flight dynamics involve uncertainties in parameters, aerodynamic derivatives, and engine thrust. These uncertainties can be categorized into three types: known-predictable, known-unpredictable, and unknown. While advanced control systems typically rely on high-fidelity dynamical models in dealing with known-predictable uncertainties, simplified approaches are used for the second and third categories to manage the complexities involved in synthesis and implementation. In this paper, the focus is on accurately modeling the internal dynamics, which primarily deal with parametric uncertainties. Real-time data is employed to identify uncertainties in the remaining external dynamics, including both known-unpredictable and unknown aspects. To address these uncertainties and maintain optimal performance, stability, and robustness, the authors propose a framework known as Sequential Data-Assisted Control (SDAC). This framework involves using a model-based nonlinear controller for the internal dynamics to provide the desired momentum to a data-based controller responsible for the external dynamics. By the momentum through the internal dynamics and leveraging the Koopman operator, the linear evolution of momentum is derived. This information is then utilized by the data-based controller to assign appropriate control inputs. The proposed approach establishes a novel foundation for a comprehensive analysis of maneuverability, stabilizability, and controllability. To evaluate the performance of SDAC, closed-loop simulations are conducted using the NASA Generic Transport Model (GTM). The data-based controller employs Linear Quadratic Regulator (LQR), while the model-based controller uses robust sliding mode control. A comparison is made with a pure robust nonlinear controller, demonstrating significant performance improvements, particularly in cases involving known-unpredictable uncertainties.