RBFNN-Based Adaptive Event-Triggered Control for Heterogeneous Vehicle Platoon Consensus
Zhibei Wu, Jitao Sun, Shanshan Hong
Abstract
Vehicle platoon, as an important application of connected automated vehicles (CAVs), is a hot topic in the intelligent transportation systems (ITSs). Most existing results on platoon control used the exact feedback linearization method to transform nonlinear vehicle dynamics into linear ones, which required complete prior knowledge of parameters. As the measurement errors and the environment disturbance exist, the accurate parameters are difficult to obtain in real world. This paper considers a nonlinear uncertain vehicle platoon under a network topology (NT) modeled by a spanning tree in a digraph. Radial basis function neural networks (RBFNNs) are used to approximate the uncertain function and an adaptive distributed controller is designed by backstepping control method. To reduce the update rate of the controllers, an event-triggered control scheme is proposed with two different kinds of triggering conditions. It is proven that vehicle platoon practical consensus can be achieved under the Zeno-free adaptive event-triggered control scheme. Moreover, for bidirectional (BD) NT and two predecessor-following (TPF) NT, vehicle platoon reaches the string stability. Finally, a vehicle platoon under three different kinds of NTs is simulated to demonstrate the effectiveness of the theoretical results.