快速路交通流常微分模型的PD型迭代学习控制

PD-type iterative learning control for freeway traffic flow ordinary differential equation model

针对快速路交通常微分模型,利用快速路交通流具有明显的重复性和周期性等特点,首先,设计了一种快速路交通流常微分模型的入口匝道交通流PD型迭代学习控制算法,并建立了迭代学习误差的收敛性条件;其次,利用λ范数和Bellman-Gronwall引理证明了算法的收敛性.最后,通过数值仿真结果进一步说明了提出的迭代学习控制方法的有效性.

Based on the freeway traffic flow ordinary differential equation model,this paper presents the PD-type iterative learning control approach of the on-ramp traffic flow for freeway traffic flow ordinary differential equation model with consideration of the repeatability and periodicity of traffic system, and onvergence condition of iterative learning error is established. Furthermore, the convergence of the algorithm is proved by using norm and Bellman-Gronwall lemma. The results of numerical simulation further show the effectiveness of the proposed approach.