Experimental machine learning algorithm validated with drone simulated data

So one of the main objectives of my PhD research was to achieve the difficult task of developing a learning algorithm for machine learning (RBF neural networks to be more specific) applications, that would enable the prediction of drone propeller damage in real-time AND without altering the bought-out flight controller (DJI Naza, APM, Pixhawk, etc...). The only way it could achieve that was by analyzing the outputs of the flight controller sensors and learn when a fault would occur.

Well, I believe I'm getting closer to this objective (submission is Nov 2019). I've decided to include the two figures below which illustrates the training process (0.2 sec on desktop) and prediction time (0.008 sec) and the accuracy to the true dynamics of the quadcopter drone. In this case the pitch dynamics are being predicted. Although noise hasn't been introduced, it's quite clear from the graphs, that the learning algorithm has enabled the RBF network to accurately capture the dynamics of the quadcopter within a short time period.

This research milestone (quite a big one I might add) has now served as foundation to build a framework for developing more robust/complex machine learning algorithms with the aim of demonstrating the use of machine learning in a real-time/low-cost sense, which could be used to alert when a dynamic system (such as a drone) appears to have a fault.

Exciting times ahead...

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