TY - GEN
T1 - Collision-Free Model Predictive Control for Periodic Trajectory Tracking of UAVs
AU - Huo, Da
AU - Dai, Li
AU - Wang, Peizhan
AU - Xue, Ruochen
AU - Xia, Yuanqing
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2023
Y1 - 2023
N2 - This article focuses on the periodic trajectory tracking problem of UAVs in an environment with both static obstacles and moving obstacles, in which the reference trajectory allows to be unreachable. This problem is solved by integrating the trajectory planner with trajectory tracking controller in a single MPC optimization problem. So as to achieve the trajectory tracking task safely, additional variables are introduced into the MPC optimization problem to generate an optimal reachable trajectory to track. To be able to avoid collisions with obstacles, MINVO basis is used to obtain an outer polyhedral approximation of obstacles’ trajectories, and collision-free constraints are then derived by optimizing planes separating the polyhedrons from the trajectory of UAV. The MPC optimization formulated can be solved by a standard nonlinear programming solver. Finally, the efficacy of the proposed control algorithm is demonstrated by a simulation example for UAVs in a limited environment with obstacles.
AB - This article focuses on the periodic trajectory tracking problem of UAVs in an environment with both static obstacles and moving obstacles, in which the reference trajectory allows to be unreachable. This problem is solved by integrating the trajectory planner with trajectory tracking controller in a single MPC optimization problem. So as to achieve the trajectory tracking task safely, additional variables are introduced into the MPC optimization problem to generate an optimal reachable trajectory to track. To be able to avoid collisions with obstacles, MINVO basis is used to obtain an outer polyhedral approximation of obstacles’ trajectories, and collision-free constraints are then derived by optimizing planes separating the polyhedrons from the trajectory of UAV. The MPC optimization formulated can be solved by a standard nonlinear programming solver. Finally, the efficacy of the proposed control algorithm is demonstrated by a simulation example for UAVs in a limited environment with obstacles.
KW - Collision avoidance
KW - Model predictive control (MPC)
KW - Trajectory tracking
KW - Unmanned aerial vehicle (UAV)
UR - http://www.scopus.com/inward/record.url?scp=85151154630&partnerID=8YFLogxK
U2 - 10.1007/978-981-19-6613-2_128
DO - 10.1007/978-981-19-6613-2_128
M3 - Conference contribution
AN - SCOPUS:85151154630
SN - 9789811966125
T3 - Lecture Notes in Electrical Engineering
SP - 1291
EP - 1300
BT - Advances in Guidance, Navigation and Control - Proceedings of 2022 International Conference on Guidance, Navigation and Control
A2 - Yan, Liang
A2 - Duan, Haibin
A2 - Deng, Yimin
A2 - Yan, Liang
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Guidance, Navigation and Control, ICGNC 2022
Y2 - 5 August 2022 through 7 August 2022
ER -