Data‐assisted control: A framework development by exploiting NASA Generic Transport platform

Abstract

The increasing capabilities of control systems, driven by abundant data and computational resources, necessitate the incorporation of data‐based techniques with model‐based approaches. While mathematical models exist for air and space vehicles, they may not fully meet the requirements for expanding control system capabilities. Consequently, the development of data‐based extensions in collaboration with these models becomes crucial to provide specific analysis and synthesis tools in the hybrid realm for practical cases. This article introduces the data‐assisted control (DAC) framework for aerospace vehicles, with the NASA generic transport model serving as the platform for study. By leveraging data, the model‐based controller enhances performance during a damage event. Real‐time decisions are made to update the control law based on information obtained from the data, while the model‐based controller may not exhibit consistent performance. The closed‐loop system is shown to be stable in the transition phase between the data and the model, and robust against uncertainties. The dual unscented Kalman filter is employed to estimate fixed dynamic parameters and generalized force‐moment, and the Koopman estimator is used to estimate the evolution equation of the estimated forces and moments. The dual estimation model ensures observability by considering the persistency of excitation. 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. The DAC framework offers a robust approach to integrate data and models, significantly enhancing control performance in practical aerospace applications.