Abstract

A methodology for the experimental modelling of the electric actuators of a multirotor is presented in this work. These actuators are usually brushless DC motors which are driven by electronic speed controllers in an open loop. The duty cycle of a PWM signal, generated by the electronic control unit, is the input of the electronic controller. However, during the control design procedure for the multirotor, it is important to account with a model of the actuators as its dynamical features define the closed-loop performance of the overall aircraft. Hence, a procedure, based on low-cost electronic components, to obtain approximated transfer functions of the actuators of a multirotor is presented. Moreover, as the proposed signal processing algorithms are simple, the computational capabilities of the required embedded system are also low. Given that different control schemes require different information from the actuator, two models were obtained: a duty cycle vs. angular velocity transfer function and a duty cycle vs. consumed current transfer function. The effectivity of the proposal is validated with experimental results on common electric actuators of a multirotor.

Highlights

  • Several research and application projects have been developed based on the capabilities of an unmanned aerial vehicle (UAV)

  • The dynamical model of a brushless direct current (BLDC) motor, given by equation (7), was implemented in Simulink using the parameters of Table 1

  • The electronic speed controllers (ESC) is injected with a PWM signal of a 100% duty cycle for a period, and, later, the duty cycle is decreased to 50%

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Summary

Introduction

Several research and application projects have been developed based on the capabilities of an unmanned aerial vehicle (UAV). There are some control schemes for UAVs that do not need the knowledge of the dynamic model of the vehicle or any of its subsystems [3]. Most of the controllers used for this type of vehicles require a total or partial knowledge of their dynamical characteristics. Those controllers oriented to increase the robustness of the system’s closed-loop behavior by means of sliding modes control [4, 5], adaptive control [6, 7], or neural networks [8]. In [13, 14], respective methodologies to the model identification of these type of actuators were developed and applied in real-time experiments.

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