A Zeroing Neural Network-Based Approach to Parameter-Varying Platooning Control of Connected Automated Vehicles

This article is concerned with platooning control of connected automated vehicles. A novel parameter-varying zeroing neural network-based approach is proposed for vehicles to track a virtual signal, while maintaining a desired distance between every two consecutive vehicles. Specifically, distributed parameter-varying observers are first designed to track the virtual signal. Then by using the observers and constructing a nonlinear manifold, a parameter-varying finite-time controller is developed to achieve the platooning control. The proposed parameter-varying controller has advantages of both fast response and disturbance rejection. Finally, numerical examples of five vehicles under different communication graphs are provided to demonstrate the effectiveness of the proposed controllers.