Adaptive non‐linear coordinated optimal dynamic platoon control of connected autonomous distributed electric vehicles on curved roads
Author(s) -
Guo Jinghua,
Jingyao Wang,
Li Keqiang,
Luo Yugong
Publication year - 2020
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2020.0112
Subject(s) - platoon , computer science , electric vehicle , control (management) , vehicle dynamics , automotive engineering , control engineering , engineering , control theory (sociology) , artificial intelligence , physics , quantum mechanics , power (physics)
A novel adaptive coordinated platoon control of connected autonomous distributed electric vehicles (CADEVs) on curved roads is proposed to increase traffic security. The nonlinear vehicle dynamic model that can precisely reflect the driving behaviours of CADEVs on curved roads is deduced by the Newton–Euler method. Owing to the fact that CADEVs have the strong coupled, uncertain non‐linear and over‐actuated features, a novel disturbance observer‐based adaptive coordinated optimal dynamic platoon control strategy is presented to supervise the lateral and longitudinal coupled motions of CADEVs on curved roads, in which the switching gains of backstepping sliding mode control term are precisely adjusted by the neural‐network technique, and the uniform ultimate boundedness of closed‐loop high‐level control system is guaranteed through the Lyapunov stability theory. Then, a sequential quadratic programming (SQP) tire distributor is the basic component of the low‐level control law, which can realise the dynamic control distribution of over‐actuated tire actuators of CADEVs. Finally, the results manifest the effectiveness of the proposed adaptive coordinated platoon control strategy.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom