Data-Driven Dynamic Event-Triggered Fault-Tolerant Platooning Control

Abstract

This paper addresses the dynamic event-triggered (DET) fault-tolerant model-free adaptive platooning control (MFAPC) problem of vehicle platoon systems subject to sensor faults. First of all, a novel redefined output is introduced to assist in proving the synchronous tracking of the position and velocity. Based on which, the equivalent dynamic linearization technique is used for transforming the nonlinear vehicular platooning systems into an linear data model. Then, an observer is designed to introduce the estimation of the pseudo-partial derivative (PPD) parameter, which can also eliminate the symbol restriction on it. In addition, a DET mechanism is introduced to save network resources more effectively, and the neural network method is applied to deal with the sensor faults for the safe vehicle driving. Further, a novel DET-based fault-tolerant MFAPC strategy is developed to realize the vehicular position and velocity tracking only using the input-output data. At last, an example is provided to prove the effectiveness of designed MFAPC framework.