Title: Real-time system identification of an unmanned quadcopter system using fully tuned radial basis function neural networks

Authors: Mohammad Fahmi Pairan; Syariful Syafiq Shamsudin; Mohd Fauzi Yaakub; Mohd Shazlan Mohd Anwar

Addresses: Research Center for Unmanned Vehicles, Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia ' Research Center for Unmanned Vehicles, Faculty of Mechanical and Manufacturing Engineering, Department of Aeronautical Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia ' Research Center for Unmanned Vehicles, Faculty of Mechanical and Manufacturing Engineering, Department of Aeronautical Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia ' Virtual Instrument and System Innovation Sdn Bhd, 47301 Petaling Jaya, Selangor, Malaysia

Abstract: In this paper, we present the performance analysis of a fully tuned neural network trained with the extended minimal resource allocating network (EMRAN) algorithm for real-time identification of a quadcopter. Radial basis function network (RBF) based on system identification can be utilised as an alternative technique for quadcopter modelling. To prevent the neurons and network parameters selection dilemma during trial and error approach, RBF with EMRAN training algorithm is proposed. This automatic tuning algorithm will implement the network growing and pruning method to add or eliminate neurons in the RBF. The EMRAN's performance is compared with the minimal resource allocating network (MRAN) training for 1000 input-output pair untrained attitude data. The findings show that the EMRAN method generates a faster mean training time of roughly 4.16 ms for neuron size of up to 88 units compared to MRAN at 5.89 ms with a slight reduction in prediction accuracy.

Keywords: quadcopter; system identification; neural network; fully tuned neural network; RBF; radial basis function.

DOI: 10.1504/IJMIC.2021.120209

International Journal of Modelling, Identification and Control, 2021 Vol.37 No.2, pp.128 - 139

Received: 17 May 2020
Accepted: 25 Dec 2020
Published online: 11 Jan 2022 *

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