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

• Published in: IEEE transactions on circuits and systems. II, Express briefs Vol. 71; no. 3; p. 1
• Main Authors: Kang, Hyun Ho, Ahn, Choon Ki
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Artificial neural networks; Autonomous aerial vehicles; finite-memory; Iterative methods; iterative online learning; Mathematical models; Neural networks; Real time; recurrent neural network (RNN); Robustness; System identification; Training; Unmanned aerial vehicles; unmanned aerial vehicles (UAVs); Weight measurement
• ISSN: 1549-7747; 1558-3791
• DOI: 10.1109/TCSII.2023.3325831

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.