Data-Assisted Control -- A Framework Development by Exploiting NASA GTM Platform

Today's focus on expanding the capabilities of control systems, resulting from the abundance of data and computational resources, requires data-based alternatives over model-based ones. These alternatives may become the sole tool for analysis and synthesis. Nevertheless, mathematical models are available to some extent, especially for air and space vehicles. Hypothetically, data assistance would be the approach to meet the requirements in collaboration with the model. In this paper, a framework of Data-Assisted Control (DAC) for aerospace vehicles is proposed. NASA Generic Transport Model (GTM) is the platform for the study and the data supports the model-based controller in extending performance over a damage event. The framework requires real-time decisions to override the control law with the information obtained from the data, while the model-based controller does not show regular performance. The closed-loop system is shown to be stable in the transition phase between the data and the model. The fixed dynamic parameters are estimated using the Dual Unscented Kalman Filter (DUKF) and the evolution of the generalized force moments is estimated using the Koopman estimator. Simulations have shown that the purely model-based robust control leads to degradation of the closed-loop performance in case of damage, suggesting the need for data assistance.