Two-Stage Iterative Finite-Memory Neural Network Identification for Unmanned Aerial Vehicles.

Recently, a system identification technique called FiMos-TA SV11 has been introduced for the accurate identification of unmanned aerial vehicles (UAVs). This approach offers impressive performances, such as robustness and accuracy against disturbances and error accumulation, by utilizing a finite-memory-based training scheme. However, a major limitation of FiMos-TA 1 is that it requires inverse matrix operations on large matrices to obtain training gains, which severely affects its real-time implementation performance. Moreover, it relies on the amount of variations in the initial weights and the measurement model in finite-memory is insufficiently adaptable to changing conditions. Therefore, we propose a new approach called the two-stage iterative finite-memory neural network (TSIFNN) identification strategy for UAVs that overcomes all the limitations of FiMos-TA 1, ensuring not only robustness and accuracy but also real-time performance. We demonstrate the real-time performance, robustness, and accuracy of the proposed TSIFNN identification through a UAV experiment.