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.

基于反步技术，本文设计了一种基于观测器的事件触发自适应编队控制算法，用于具有运动不确定性（例如，未知的自动驾驶汽车质量、内阻和外部干扰）的自动驾驶汽车（AV）。为了避免自动驾驶汽车之间传输过多的多车辆状态信息（即速度、位置等），所提出的自适应队列控制算法仅使用不精确采样的自动驾驶汽车位置。设计了一种新颖的采样观测器，将不精确采样的 AV 位置转换为 AV 速度和位置。引入具有固定事件触发阈值的事件触发机制来降低AV控制律的更新频率。通过新构造的Lyapunov函数，自适应排行控制算法可以实现期望位置和速度轨迹的同时跟踪，并且控制律的更新时间间隔存在大于或等于采样时间间隔的下界的职位。数值仿真表明，自适应编队控制算法可以提前控制异构自动驾驶汽车排成线性/方形队形避障，并且该排中的所有异构自动驾驶汽车可以同时跟踪预期位置和速度轨迹。另外，AV控制律的更新时间间隔比AV位置的采样时间间隔长。