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Event‐triggered model predictive control for multi‐vehicle systems with collision avoidance and obstacle avoidance
Author(s) -
Yang Hongjiu,
Li Qing,
Zuo Zhiqiang,
Zhao Hai
Publication year - 2021
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.5551
Subject(s) - collision avoidance , obstacle avoidance , model predictive control , control theory (sociology) , computer science , obstacle , constraint (computer aided design) , event (particle physics) , collision , control (management) , engineering , artificial intelligence , mobile robot , computer security , mechanical engineering , political science , robot , law , physics , quantum mechanics
In this article, event‐triggered model predictive control is used for simultaneous tracking and formation of a multi‐vehicle system with collision avoidance and obstacle avoidance. An event‐triggered mechanism is established to reduce computational burden in the model predictive control strategy. A compatibility constraint is proposed to guarantee collision avoidance and convergence for the multi‐vehicle system by limiting an uncertainty deviation of each vehicle. Between each vehicle and obstacles, a safe distance is ensured by a robust obstacle avoidance constraint. Finally, effectiveness and advantages of the proposed strategy are shown by two simulation examples.