Frequency-Domain Estimation of a Transfer Matrix of an Uncommon Quadrotor in Hover


Baskin M., LEBLEBİCİOĞLU M. K.

IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Publication Date: 2022
  • Doi Number: 10.1109/tcst.2022.3185936
  • Journal Name: IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, Civil Engineering Abstracts
  • Keywords: Rotors, Uncertainty, Vehicle dynamics, Robust control, Atmospheric modeling, Helicopters, Estimation, Attitude control, flight control, model validation, multirotors, robust control, system identification, SYSTEM-IDENTIFICATION, ROBUST-CONTROL
  • Middle East Technical University Affiliated: Yes

Abstract

In this article, system identification of an uncommon quadrotor in hover is discussed. Two counter-rotating big rotors on the longitudinal axis and two counter-rotating small tilt rotors on the lateral axis form this quadrotor configuration. First, the nonlinear dynamic model of this vehicle is derived from Newton-Euler formulation. Next, using the approximate linear hover model and the suitable rotor mixing matrix for axes decoupling, simplified attitude dynamics of this quadrotor are obtained. However, the effects of sensor delays, flexible modes of the airframe, and inexact decoupling are not visible in this diagonal model, which is mainly based on physical principles. Therefore, a system identification method is used to obtain a more accurate model, which is required for control design. Hence, during parametric identification of nominal model coprime factors, a small robust control criterion is targeted. Then, the frequency response function of the proposed quadrotor prototype in hover is obtained. Next, the linear parametric model of the vehicle is estimated by solving optimally conditioned least squares and subsequent Gauss-Newton problems. After that, using validation-based uncertainty quantification, the uncertain model set is constructed around the estimated coprime factors. The resulting model set and its robust control relevance are depicted.