Observer-based event-triggered adaptive platooning control for autonomous vehicles with motion uncertainties

Based on the back-stepping technique, this paper designs an observer-based event-triggered adaptive platooning control algorithm for autonomous vehicles (AVs) with motion uncertainties (e.g., unknown AV mass, internal resistance, and external disturbances). To avoid the transmission of excessive multi-vehicle status information (i.e., speed, position, and so on) between AVs, the adaptive platooning control algorithm proposed only uses the imprecise sampled AV positions. A novel sampling observer designed converts the imprecise sampled AV positions into AV speed and position. The event-triggered mechanism with a fixed event-triggered threshold is introduced to reduce the update frequency of AV control laws. Through the newly constructed Lyapunov function, the adaptive platooning control algorithm can achieve the simultaneous tracking of expected position and speed trajectories, and there is a lower bound on the update time interval of the control laws that is greater than or equal to the sampling time interval of the positions. Numerical simulation demonstrates that the adaptive platooning control algorithm can control a heterogeneous AV platoon in a linear/square formation in advance for obstacle avoidance, and that all heterogeneous AVs in the platoon can track the expected position and speed trajectories simultaneously. Additionally, the update time interval of AV control laws is longer than the sampling time interval of AV positions.