TY - JOUR
T1 - Efficient Nonlinear Model Predictive Control for Quadrotor Trajectory Tracking
T2 - Algorithms and Experiment
AU - Wang, Dong
AU - Pan, Quan
AU - Shi, Yang
AU - Hu, Jinwen
AU - Records, Chunhui Zhao
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2021/10/1
Y1 - 2021/10/1
N2 - This article studies an efficient nonlinear model-predictive control (NMPC) scheme for trajectory tracking control of a quadrotor unmanned aerial vehicle (UAV). By augmenting the desired trajectory to a reference dynamical system, we can make the tracking task fit into the standard NMPC framework. In order to alleviate the heavy computational burden caused by solving the corresponding NMPC optimization problem online, we develop an improved continuation/generalized minimal residual ( ${i}\text{C}$ /GMRES) algorithm. Compared with the standard C/GMRES method, the inequality constraint is relaxed by imposing the penalty term on the cost function. To guarantee the closed-loop system stability, we introduce a contraction constraint. Based on the proposed numerical algorithm and the stability constraint, we develop a novel efficient-NMPC algorithm to achieve acceptable control performance with reduced computational complexity. The numerical convergence of ${i}\text{C}$ /GMRES solutions and the closed-loop stability of efficient-NMPC are theoretically analyzed in the presence of the input constraint. Finally, the numerical simulations, software-in-the-loop (SIL) simulations, and the real-time experiment are given to demonstrate the effectiveness of the proposed ${i}\text{C}$ /GMRES algorithm and efficient-NMPC scheme.
AB - This article studies an efficient nonlinear model-predictive control (NMPC) scheme for trajectory tracking control of a quadrotor unmanned aerial vehicle (UAV). By augmenting the desired trajectory to a reference dynamical system, we can make the tracking task fit into the standard NMPC framework. In order to alleviate the heavy computational burden caused by solving the corresponding NMPC optimization problem online, we develop an improved continuation/generalized minimal residual ( ${i}\text{C}$ /GMRES) algorithm. Compared with the standard C/GMRES method, the inequality constraint is relaxed by imposing the penalty term on the cost function. To guarantee the closed-loop system stability, we introduce a contraction constraint. Based on the proposed numerical algorithm and the stability constraint, we develop a novel efficient-NMPC algorithm to achieve acceptable control performance with reduced computational complexity. The numerical convergence of ${i}\text{C}$ /GMRES solutions and the closed-loop stability of efficient-NMPC are theoretically analyzed in the presence of the input constraint. Finally, the numerical simulations, software-in-the-loop (SIL) simulations, and the real-time experiment are given to demonstrate the effectiveness of the proposed ${i}\text{C}$ /GMRES algorithm and efficient-NMPC scheme.
KW - C/GMRES
KW - input constraints
KW - nonlinear model predictive control (NMPC)
KW - real-time implementation
KW - trajectory tracking
UR - http://www.scopus.com/inward/record.url?scp=85099730826&partnerID=8YFLogxK
U2 - 10.1109/TCYB.2020.3043361
DO - 10.1109/TCYB.2020.3043361
M3 - 文章
C2 - 33471775
AN - SCOPUS:85099730826
SN - 2168-2267
VL - 51
SP - 5057
EP - 5068
JO - IEEE Transactions on Cybernetics
JF - IEEE Transactions on Cybernetics
IS - 10
ER -