A Systematic Modelling Framework for Commercial Unmanned Hexacopter Considering Fractional Order System Theory

This research work proposes a systematic methodology to model Commercial-Off-The-Shelf Unmanned Aerial Vehicle (UAVs) that belong to the class of multicopters. The modelling framework consists of meticulous analysis of frequency domain and time domain data acquired from the hexacopter. The aim of this work is to model a commercially available UAV to enable the development of an efficient outer loop controller, specific to the intended application. This framework makes use of sine and step response data of the hexacopter’s velocities in all the three planar axes corresponding to the global frame of reference. The framework considers the fractional-order model as a potential candidate for the UAV. Hence, it consists of a systematic method to determine the order of the model in integers or fractions, and its parameters. In the modelling process, control theory and fractional order theory have been sufficiently used to best correlate theoretical proposition with experimental outcomes. Also, real world factors and system constraints have been explicated and considered appropriately. The model obtained using the proposed framework has an average error of less than 5% when compared to the real system’s response. Additionally, the ill effect of considering a near integer order model for a fractional order system is demonstrated to show the importance of this modelling framework. Further, a linear controller is implemented for the system using the estimated model, and corroborated with real world results.

[1]  Saptarshi Das,et al.  Online identification of fractional order models with time delay: An experimental study , 2011, 2011 International Conference on Communication and Industrial Application.

[2]  James Humbert,et al.  System Identification of a Quadrotor Micro Air Vehicle , 2010 .

[3]  Liu Yang,et al.  Parameter identification for a quadrotor helicopter using PSO , 2013, 52nd IEEE Conference on Decision and Control.

[4]  Yang Gao,et al.  End-to-End Learning of Driving Models from Large-Scale Video Datasets , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Ameya Anil Kesarkar,et al.  Asymptotic magnitude Bode plots of fractional-order transfer functions , 2019, IEEE/CAA Journal of Automatica Sinica.

[6]  Wei Wei,et al.  Frequency-Domain System Identification and Simulation of a Quadrotor Controller , 2014 .

[7]  I. Podlubny Fractional differential equations : an introduction to fractional derivatives, fractional differential equations, to methods of their solution and some of their applications , 1999 .

[8]  Mohammad Saleh Tavazoei,et al.  Simple Fractional Order Model Structures and their Applications in Control System Design , 2010, Eur. J. Control.

[9]  Sören Hohmann,et al.  Online Parameter Identification of a Fractional Order Model , 2018, 2018 IEEE Conference on Decision and Control (CDC).

[10]  Yangquan Chen,et al.  A Survey and Categorization of Small Low-Cost Unmanned Aerial Vehicle System Identification , 2014, J. Intell. Robotic Syst..

[11]  Sreenatha G. Anavatti,et al.  Application of Extended Kalman Filter Towards UAV Identification , 2007 .

[12]  Sreenatha G. Anavatti,et al.  A UKF-NN Framework for System Identification of Small Unmanned Aerial Vehicles , 2008, 2008 11th International IEEE Conference on Intelligent Transportation Systems.

[13]  Shankar C. Subramanian,et al.  Model Based Control of Heavy Road Vehicle Brakes for Active Safety Applications , 2017, 2017 14th IEEE India Council International Conference (INDICON).

[14]  Daksh Shukla,et al.  Modeling and flight control of a commercial nano quadrotor , 2017, 2017 International Conference on Unmanned Aircraft Systems (ICUAS).

[15]  Matthew J. Rutherford,et al.  Modeling and Frequency‐Domain Parameter Identification of a Small‐Scale Flybarless Unmanned Helicopter , 2016 .

[16]  M. D. Narayanan,et al.  Parametric identification of fractional-order nonlinear systems , 2018 .

[17]  Roland Siegwart,et al.  Control of a Quadrotor With Reinforcement Learning , 2017, IEEE Robotics and Automation Letters.

[18]  Nicola Bezzo,et al.  Parameter-free Regression-based Autonomous Control of Off-the-shelf Quadrotor UAVs , 2019, 2019 International Conference on Unmanned Aircraft Systems (ICUAS).

[19]  Mohammad Haeri,et al.  Over- and under-convergent step responses in fractional-order transfer functions , 2010 .

[20]  Fendy Santoso,et al.  Modeling, Autopilot Design, and Field Tuning of a UAV With Minimum Control Surfaces , 2015, IEEE Transactions on Control Systems Technology.