A Comparative Study for Control of Quadrotor UAVs
Modeling and controlling highly nonlinear, multivariable, unstable, coupled and underactuated systems are challenging problems to which a unique solution does not exist. Modeling and control of Unmanned Aerial Vehicles (UAVs) with four rotors fall into that category of problems. In this paper, a non...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/6/3464 |
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| author | Marco Rinaldi Stefano Primatesta Giorgio Guglieri |
| author_facet | Marco Rinaldi Stefano Primatesta Giorgio Guglieri |
| author_sort | Marco Rinaldi |
| collection | DOAJ |
| description | Modeling and controlling highly nonlinear, multivariable, unstable, coupled and underactuated systems are challenging problems to which a unique solution does not exist. Modeling and control of Unmanned Aerial Vehicles (UAVs) with four rotors fall into that category of problems. In this paper, a nonlinear quadrotor UAV dynamical model is developed with the Newton–Euler method, and a control architecture is proposed for 3D trajectory tracking. The controller design is decoupled into two parts: an inner loop for attitude stabilization and an outer loop for trajectory tracking. A few attitude stabilization methods are discussed, implemented and compared, considering the following control approaches: Proportional–Integral–Derivative (PID), Linear–Quadratic Regulator (LQR), Model Predictive Control (MPC), Feedback Linearization (FL) and Sliding Mode Control (SMC). This paper is intended to serve as a guideline work for selecting quadcopters’ control strategies, both in terms of quantitative and qualitative considerations. PID and LQR controllers are designed, exploiting the model linearized about the hovering condition, while MPC, FL and SMC directly exploit the nonlinear model, with minor simplifications. The fast dynamics ensured by the SMC-based controller together with its robustness and the limited estimated command effort of the controller make it the most promising controller for quadrotor attitude stabilization. The outer loop consists of three independent PID controllers: one for altitude control and the other two, together with a dynamics’ inversion, are entitled to the computation of the reference attitude for the inner loop. The capability of the controlled closed-loop system of executing complex trajectories is demonstrated by means of simulations in MATLAB/Simulink<sup>®</sup>. |
| first_indexed | 2024-03-11T06:59:55Z |
| format | Article |
| id | doaj.art-c608eade1f1c4ae7bfa4e0e755210f61 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T06:59:55Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-c608eade1f1c4ae7bfa4e0e755210f612023-11-17T09:22:05ZengMDPI AGApplied Sciences2076-34172023-03-01136346410.3390/app13063464A Comparative Study for Control of Quadrotor UAVsMarco Rinaldi0Stefano Primatesta1Giorgio Guglieri2Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, ItalyDepartment of Mechanical and Aerospace Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, ItalyDepartment of Mechanical and Aerospace Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, ItalyModeling and controlling highly nonlinear, multivariable, unstable, coupled and underactuated systems are challenging problems to which a unique solution does not exist. Modeling and control of Unmanned Aerial Vehicles (UAVs) with four rotors fall into that category of problems. In this paper, a nonlinear quadrotor UAV dynamical model is developed with the Newton–Euler method, and a control architecture is proposed for 3D trajectory tracking. The controller design is decoupled into two parts: an inner loop for attitude stabilization and an outer loop for trajectory tracking. A few attitude stabilization methods are discussed, implemented and compared, considering the following control approaches: Proportional–Integral–Derivative (PID), Linear–Quadratic Regulator (LQR), Model Predictive Control (MPC), Feedback Linearization (FL) and Sliding Mode Control (SMC). This paper is intended to serve as a guideline work for selecting quadcopters’ control strategies, both in terms of quantitative and qualitative considerations. PID and LQR controllers are designed, exploiting the model linearized about the hovering condition, while MPC, FL and SMC directly exploit the nonlinear model, with minor simplifications. The fast dynamics ensured by the SMC-based controller together with its robustness and the limited estimated command effort of the controller make it the most promising controller for quadrotor attitude stabilization. The outer loop consists of three independent PID controllers: one for altitude control and the other two, together with a dynamics’ inversion, are entitled to the computation of the reference attitude for the inner loop. The capability of the controlled closed-loop system of executing complex trajectories is demonstrated by means of simulations in MATLAB/Simulink<sup>®</sup>.https://www.mdpi.com/2076-3417/13/6/3464unmanned aerial vehiclesfeedback linearizationsliding mode controlMPCPIDLQR |
| spellingShingle | Marco Rinaldi Stefano Primatesta Giorgio Guglieri A Comparative Study for Control of Quadrotor UAVs Applied Sciences unmanned aerial vehicles feedback linearization sliding mode control MPC PID LQR |
| title | A Comparative Study for Control of Quadrotor UAVs |
| title_full | A Comparative Study for Control of Quadrotor UAVs |
| title_fullStr | A Comparative Study for Control of Quadrotor UAVs |
| title_full_unstemmed | A Comparative Study for Control of Quadrotor UAVs |
| title_short | A Comparative Study for Control of Quadrotor UAVs |
| title_sort | comparative study for control of quadrotor uavs |
| topic | unmanned aerial vehicles feedback linearization sliding mode control MPC PID LQR |
| url | https://www.mdpi.com/2076-3417/13/6/3464 |
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