AI 中文总结
该研究在并发学习框架下提出自适应纵向车队策略，无需施加持续激励即可保证动力总成参数收敛，证明中考虑了误差动力学的额外未知增益，优于现有方案。
AI 中文摘要
本研究在并发学习框架下提出一种新型自适应纵向车队策略。自适应指车辆通过在线估计应对动力总成参数的不确定性；并发学习指估计时同时使用当前与历史数据。该策略优于现有方案，因无需对车辆行为施加持续激励即可保证收敛至真实动力总成参数，仅需存在单个非零数据样本即可；同时，本文给出的并发学习证明优于现有成果，因其考虑了误差动力学中额外的未知增益。
英文摘要
This work proposes a new adaptive longitudinal platooning strategy in the framework of concurrent learning. Adaptive refers to vehicles facing uncertainty in powertrain parameters via on-line estimation; concurrent learning refers to using both current and past data in the estimation. The proposed platooning strategy advances existing ones since convergence to the true powertrain parameters is guaranteed without imposing persistence of excitation on the vehicle behavior: it suffices the presence of a single non-zero data sample. Meanwhile, the concurrent learning proof we give advances existing ones since it takes into account an extra unknown gain in the error dynamics.
Journal refIEEE Control Systems Letters, vol. 8, pp. 303-308, 2024
DOI：10.1109/LCSYS.2023.3333004
