Data-Assisted Control -- A Framework Development by Exploiting NASA GTM Platform
Mostafa Eslami, Afshin Banazadeh
TL;DR
The paper addresses fragility of purely model-based aerospace control in the presence of damage and uncertainties by proposing Data-Assisted Control (DAC), which couples a Lyapunov-based nonlinear velocity regulator with a Dual UKF for fixed-dynamics parameter estimation and a Koopman-based estimator for generalized forces. A real-time decision-maker blends data-driven insights with the model, controlled by a factor $\lambda$ that transitions between the two regimes while preserving stability via a skew-symmetric property of the evolving mass matrix term. The approach is instantiated on NASA GTM and validated through simulations showing improved performance under damage versus a purely model-based controller, with observability shown to be adequate and Koopman estimation offering robust handling of high-frequency nonlinear terms. The work demonstrates the practicality of using data assistance to extend model-based control in aerospace, establishing a framework for stable, adaptive fault-tolerant control with potential for real-time implementation and future data-driven extensions.
Abstract
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.