Distributed Robust Event-Triggered Platooning Control of Connected Vehicles With Uncertain Dynamics: A Neuro-adaptive Approach

This article aims to address the distributed robust platooning control of connected automated vehicles (CAVs) with general unknown uncertain dynamics. Despite recent progress in this area, achieving the objective of distributed robust platooning control for CAVs with limited communication resources and uncertain dynamics is an outstanding problem. To solve such a problem, a new Zeno-free event-triggered scheme is successfully established to determine whether the vehicle's state should be sampled and transmitted among the interacting vehicles. An adaptive law for updating the weighting matrix for the neural network approximator is designed, where the relative state variables are utilized only at triggered instants. Moreover, such a neuro-adaptive approach incorporates a low-pass filter structure to effectively mitigate undesirable high-frequency oscillations that may arise with the application of high-gain learning rates. Following this, a new class of distributed event-based neuro-adaptive control protocols is meticulously designed to guarantee the uniform ultimate boundedness of spacing error, relative velocity, and relative acceleration of the whole platoon. Finally, simulation examples with different scenarios are conducted, and it is interesting to find that the proposed protocol has a lower average communication rate than traditional ones without the low-pass filter structure.