An adaptive dynamic programming method for observer‐based sliding mode control of connected vehicles subject to deception attacks

This article investigates the problem of optimal observer‐based sliding mode control (SMC) of connected vehicles subject to deception attacks and disturbances with adaptive dynamic programming (ADP) method. For a group of vehicles with unknown nonlinear dynamics term and disturbance, this article ai...

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Veröffentlicht in:International journal of robust and nonlinear control Jg. 34; H. 6; S. 3659 - 3678
Hauptverfasser: Xu, Yangguang, Guo, Ge, Yu, Shuanghe
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Bognor Regis Wiley Subscription Services, Inc 01.04.2024
Schlagworte:
Acceleration
adaptive dynamic programming
connected vehicles
Control methods
cyber‐attacks
Dynamic programming
Dynamic stability
neural network
Neural networks
Nonlinear dynamics
Optimal control
Sliding mode control
Vehicles
ISSN:1049-8923, 1099-1239
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Zusammenfassung:This article investigates the problem of optimal observer‐based sliding mode control (SMC) of connected vehicles subject to deception attacks and disturbances with adaptive dynamic programming (ADP) method. For a group of vehicles with unknown nonlinear dynamics term and disturbance, this article aims to give a control methodology to achieve secure tracking of the desired spacing, velocity and acceleration. A neural network (NN) and an observer are constructed to estimate the unknown nonlinear term and the states, respectively. Then, a SMC scheme incorporating NN approximation is developed and an off‐policy ADP method is used to implement the optimal control of sliding mode dynamics. The proposed method can ensure individual stability and string stability of the set of vehicles. Numerical simulations are conducted to demonstrate the validity of the proposed controller.
Bibliographie:ObjectType-Article-1
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ISSN:1049-8923
1099-1239
DOI:10.1002/rnc.7155