System Identification Method Based on Interpretable Machine Learning for Unknown Aircraft Dynamics

Data-driven discovery of dynamics via machine learning has become the frontier work of modeling and control, and also provides great help for expanding the application range of model-based control methods. However, many machine learning methods need huge training data, and the generalization ability outside the training area is limited. These factors impede the development of machine learning in fields such as aerospace. To solve this problem, a new interpretable learning algorithm for aircraft systems is studied, which can consider the influence of control input and be implemented online to respond quickly to the system changes. Simulation results show that compared with the conventional neural network method, the proposed algorithm has higher performance, fewer data demands, and higher computational efficiency. Finally, the model identified online by the proposed algorithm is used in the model-based controller to further verify the effectiveness of this algorithm.