Finite-Time Multi-Lane Fusion Control for 2-D Plane Vehicle Platoon With FDI Attacks

This paper proposes a finite-time prescribed performance control method to ensure effective two-dimensional (2-D) plane vehicle platoon multi-lane fusion and maintain platoon performance in the presence of unknown dynamic uncertainties and false data injection (FDI) attacks. Firstly, unknown dynamic uncertainties of the vehicle are approximated using radial basis function neural networks (RBFNNs). Building on this neural network approximation under FDI attacks, a novel state observer is developed to estimate the vehicle’s state, restore the communication protocol when the communication link is attacked, and address the complex coupling issues between vehicle states. Furthermore, finite-time prescribed performance control inputs are designed based on the constructed sliding surfaces to ensure practical finite-time stability of the 2-D plane vehicle platoon. This method facilitates vehicle multi-lane fusion within a finite time while guaranteeing platoon performance and preventing collisions. Finally, numerical simulations and comparative analyses are presented to demonstrate the effectiveness and superiority of the proposed control strategy, involving one leader vehicle and six followers.