Event-Triggered Adaptive Control for Heterogeneous Vehicle Platoon System With Faulty Output Feedback

This paper focuses on the adaptive neural network output feedback control problem for heterogeneous vehicle platoon system (HVPS) under the condition of sensor deception attack. Firstly, an event-triggered mechanism by double-channel transmissions is constructed, which considers both controller triggering and output triggering, effectively reducing the waste of communication resources and unnecessary data transmission. Secondly, in order to estimate the unmeasured states in the system, a neural network state observer is designed. It takes the output signals caused by output triggering and sensor failures as input to generate usable state estimates and ensure the smooth operation of the backstepping method. In addition, barrier Lyapunov functions (BLFs) are used to impose constraints on vehicle spacing errors to ensure that the errors will not expand infinitely. Combined with dynamic surface control (DSC) technology, an adaptive event-triggered output feedback control scheme based on backstepping is proposed in this paper. It guarantees both individual vehicle stability and weak string stability of HVPS. Through Lyapunov stability analysis, it is proved that all signals remain bounded. Finally, a simulation example verifies the effectiveness of the proposed method.