Memory Sampling Position-Based Platooning Control for Autonomous Vehicles with Actuator Saturation

Using the memory sampled-data method, an elegant platooning control law is developed for an autonomous vehicle network with non-zero leader vehicle control input and actuator saturation. Specifically, the proposed controller relies solely on the current and recent position information of the vehicles. The goal is to establish a stable platoon formation by designing a suitable control input for the follower vehicle. Sufficient conditions for the desired platoon are then derived and expressed as linear matrix inequalities based on the Lyapunov-Krasovskii functional and exponential Wirtinger's integral inequality. A simulation example based on the one leader five follower vehicle model is presented to validate the correctness of the developed theoretical results.