An optimal hybrid quadcopter control technique with MPC-based backstepping

• Autorzy: Nwafor Solomon C. , Eneh Joy N. , Ndefo Mmasom I. , Ugbe Oluchi C. , Ugwu Henry I. , Ani Ozoemena

Identyfikatory

DOI

10.24425/acs.2024.149651

• Języki publikacji: EN

Abstrakty

EN

Quadcopter unmanned aerial vehicle is a multivariable, coupled, unstable, and underactuated system with inherent nonlinearity. It is gaining popularity in various applications and has been the subject of numerous research studies. However, modelling and controlling a quadcopter to follow a trajectory is a challenging issue for which there is no unique solution. This study proposes an optimal hybrid quadcopter control with MPC-based backstepping control for following a reference trajectory. The outer-loop controller (backstepping controller) regulates the quadcopter’s position, whereas the inner-loop controller (Model Predictive Control) regulates its attitude. The translational and rotational dynamics of the quadcopter are analyzed utilizing the Newton-Euler method. After that, the backstepping controller (BC) is created, which is a recurrent control method according to Lyapunov’s theory that utilizes a genetic algorithm (GA) to choose the controller parameters automatically. In order to apply a linear control technique in the presence of nonlinearities in the quadcopter dynamics, Linear Parameter Varying (LPV) Model Predictive Control (MPC) structure is developed. Simulation validated the dynamic performance of the proposed optimal hybrid MPC-based backstepping controller of the quadcopter in following a given reference trajectory. The simulations demonstrate the fact that using a command control input in trajectory tracking, the proposed control algorithm offers suitable tracking over the assigned position references with maximum appropriate tracking errors of 0.1 m for the X and Y positions and 0.15 m for the Z position.

Słowa kluczowe

EN

UAV; quadcopter; Model Predictive Control; backstepping control; linear parameter varying; genetic algorithm

• Wydawca: Polish Academy of Sciences, Committee of Automatic Control and Robotics
• Czasopismo: Archives of Control Sciences
• Rocznik: 2024
• Tom: Vol. 34, No. 1
• Strony: 39--62
• Opis fizyczny: Bibliogr. 50 poz., rys., tab., wzory

Twórcy

autor

Nwafor Solomon C.

• Department of Mechatronic Engineering, Univeristy of Nigeria, Nsukka, Enugu State, Nigeria

• https://orcid.org/0000-0003-4390-9874

autor

Eneh Joy N.

• Department of Electronicand Computer Engineering, University of Nigeria, Nsukka, Enugu State, Nigeria

• https://orcid.org/0000-0002-9937-8796

autor

Ndefo Mmasom I.

• Department of Electronicand Computer Engineering, University of Nigeria, Nsukka, Enugu State, Nigeria

• https://orcid.org/0009-0000-1961-9194

autor

Ugbe Oluchi C.

• Department of Electrical Engineering, Universityof Nigeria, Nsukka, Enugu State, Nigeria

• https://orcid.org/0000-0002-3330-6810

autor

Ugwu Henry I.

• Department of Electronicand Computer Engineering, University of Nigeria, Nsukka, Enugu State, Nigeria

• https://orcid.org/0009-0008-1076-3788

autor

Ani Ozoemena

• Department of Mechatronic Engineering and Department of Agricultural and Bioresources Engineering, Univeristy of Nigeria, Nsukka, Enugu State, Nigeria

• https://orcid.org/0000-0001-9523-9249

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Uwagi

The authors of this paper acknowledge Nigeria Tertiary Education Trust Fund (TETFUND) for funding this work as part of the 2020 TETFUND National Research Fund grant titled “Development of Multipurpose Drone and Machine Vision System for Optimal Farmland Selection/Mapping, Crop Monitoring, and Weed Management” with grant number: TETF/ES/DR&D-CE/NRF2020/SETI/88/VOL.1.