Recently the use of unmanned aerial vehicles (UAV) has been extended in diverse applications. Within the most predominant configurations are rotary wing vehicles with multiple rotors. These vehicles normally present unstable dynamic behaviour in open loop, and therefore it is necessary to implement appropriate control systems. One of the most important requirements for the design of such control systems is the aerodynamic model of the multi-rotor propulsion system. On the other hand, parametric identification is widely used in the industrial context, such as control of electric machines. In particular for the case of induction motors, the knowledge of the model parameters is essential to tune the controllers properly. Among the most successful identification algorithms in recent applications are those based on neural networks. Within this context, an innovative type of neuronal networks based on adaptive fuzzy spiking neurons (AFSNs) has captured the attention of the community due to its fuzzy neuronal characteristics for unipolar and bipolar signals, such as its fuzzy learning algorithm, a sigmoidal activation function, refractory time, axonal delay, and spikes generation. This article explores for the first time the possibility of using AFSNs for the identification of the multi-rotor UAV propulsion subsystem and the parameters of the electric subsystem of an induction motor. The results show that AFSNs are capable of identifying both systems with a high degree of precision. This opens up the possibility of using AFSNs in both, experimental aerodynamics applications for UAVs and industrial control applications.
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