Model predictive control using LPV approach for trajectory tracking of quadrotor UAV with external disturbances

被引:11
作者
Singh, Brajesh Kumar [1 ]
Kumar, Awadhesh [1 ]
机构
[1] Madan Mohan Malaviya Univ Technol, Dept Elect Engn, Gorakhpur, Uttar Pradesh, India
关键词
Quadrotor; UAV; Linear parameter varying; model predictive control (MPC); Trajectory tracking; FUZZY CONTROLLER; DESIGN;
D O I
10.1108/AEAT-12-2021-0368
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
Purpose The rotorcraft technology is very interesting area since last few decades due to variety of applications. One of the rotorcrafts is the quadrotor unmanned aerial vehicle (QUAV), which contains four rotors mounted on an airframe with an onboard controller. The QUAV is a highly nonlinear system and underactuated. Its controller design is very challenging task, and the need of controller is to make it autonomous based on mission planning. The purpose of this study is to design a controller for quadrotor UAV for attitude stabilization and trajectory tracking problem in presence of external environmental disturbances such as wind gust. Design/methodology/approach To address this problem, the model predictive control has been designed for attitude control and feedback linearization control for the position control using the linear parameter varying (LPV) approach. The trajectory tracking problem has been addressed using the circular trajectory and helical trajectory. Findings The simulation results show the efficient performance with good trajectory tracking even in presence of external disturbances in both the scenarios considered, one for circular trajectory tracking and other for helical trajectory tracking. Originality/value The novelty of the work came from using the LPV approach in controller design, which increases the robustness of the controller in presence of external disturbances.
引用
收藏
页码:607 / 618
页数:12
相关论文
共 33 条
[1]   An Efficient Model Predictive Control Scheme for an Unmanned Quadrotor Helicopter [J].
Abdolhosseini, M. ;
Zhang, Y. M. ;
Rabbath, C. A. .
JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS, 2013, 70 (1-4) :27-38
[2]   Model predictive quadrotor control: attitude, altitude and position experimental studies [J].
Alexis, K. ;
Nikolakopoulos, G. ;
Tzes, A. .
IET CONTROL THEORY AND APPLICATIONS, 2012, 6 (12) :1812-1827
[3]  
Bolandi H., 2013, INTELL CONTROL AUTOM, V4, P335, DOI [10.4236/ica.2013.43039, DOI 10.4236/ICA.2013.43039, 10.4236/ica.2013.43040]
[4]  
Bouabdallah S, 2005, IEEE INT CONF ROBOT, P2247
[5]   Modified Gaussian process regression based adaptive control for quadrotors [J].
Cen, Ruping ;
Jiang, Tao ;
Tang, Pan .
AEROSPACE SCIENCE AND TECHNOLOGY, 2021, 110
[6]   Takagi-Sugeno Dynamic Neuro-Fuzzy Controller of Uncertain Nonlinear Systems [J].
Cervantes, Jorge ;
Yu, Wen ;
Salazar, Sergio ;
Chairez, Isaac .
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2017, 25 (06) :1601-1615
[7]   Adaptive tracking control of an unmanned aerial system based on a dynamic neural-fuzzy disturbance estimator [J].
Cervantes-Rojas, Jorge S. ;
Munoz, Filiberto ;
Chairez, Isaac ;
Gonzalez-Hernandez, Ivan ;
Salazar, Sergio .
ISA TRANSACTIONS, 2020, 101 :309-326
[8]   Design of rules for in-flight non-parametric tuning of PID controllers for unmanned aerial vehicles [J].
Chehadeh, Mohamad S. ;
Boiko, Igor .
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 2019, 356 (01) :474-491
[9]   A novel nonlinear resilient control for a quadrotor UAV via backstepping control and nonlinear disturbance observer [J].
Chen, Fuyang ;
Lei, Wen ;
Zhang, Kangkang ;
Tao, Gang ;
Jiang, Bin .
NONLINEAR DYNAMICS, 2016, 85 (02) :1281-1295
[10]   Output Feedback Control of a Quadrotor UAV Using Neural Networks [J].
Dierks, Travis ;
Jagannathan, Sarangapani .
IEEE TRANSACTIONS ON NEURAL NETWORKS, 2010, 21 (01) :50-66