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Adaptive event based predictive lateral following control for unmanned ground vehicle system
Author(s) -
Zhang Hao,
Zhang Hongming,
Wang Zhuping,
Huang Chao,
Li Yan
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.5535
Subject(s) - model predictive control , control theory (sociology) , constraint (computer aided design) , computer science , computation , stability (learning theory) , path (computing) , bounded function , tracking (education) , event (particle physics) , dual (grammatical number) , control engineering , control (management) , engineering , algorithm , mathematics , artificial intelligence , mechanical engineering , psychology , mathematical analysis , pedagogy , physics , quantum mechanics , machine learning , programming language , art , literature
In this article, the lateral path following problem of unmanned ground vehicle with bounded disturbances is studied. A dual‐mode model predictive control (MPC) algorithm based on adaptive event triggered mechanism is proposed. Compared with the existing MPC algorithm, adaptive event triggered model predictive control (AEMPC) can ensure the tracking accuracy and reduce the optimization computation. Feasibility and stability of AEMPC algorithm are guaranteed by constraint region parameter design. Finally, simulation results of the vehicle dynamics model are given, and the comparisons are made.