Most perimeter control methods in literature are the model-based schemes designing the controller based on the available accurate macroscopic fundamental diagram (MFD) function with well known techniques of modern control methods. However, accurate modeling of the traffic flow system is hard and time-consuming. On the other hand, macroscopic traffic flow patterns show heavily similarity between days, and data from past days might enable improving the performance of the perimeter controller. Motivated by this observation, a model free adaptive iterative learning perimeter control (MFAILPC) scheme is proposed in this paper. The three features of this method are: (1) No dynamical model is required in the controller design by virtue of dynamic linearization data modeling technique, i.e., it is a data-driven method, (2) the perimeter controller performance will improve iteratively with the help of the repetitive operation pattern of the traffic system, (3) the learning gain is tuned adaptively along the iterative axis. The effectiveness of the proposed scheme is tested comparing with various control methods for a multi-region traffic network considering modeling errors, measurement noise, demand variations, and time-changing MFDs. Simulation results show that the proposed MFAILPC presents a great potential and is more resilient against errors than the standard perimeter control methods such as model predictive control, proportional-integral control, etc.

文献中的大多数外围控制方法是基于模型的方案，该模型基于具有现代控制方法的公知技术的可用精确宏观基本图（MFD）函数设计控制器。但是，交通流系统的准确建模既困难又费时。另一方面，宏观的流量流模式在几天之间显示出极大的相似性，并且过去几天的数据可能会提高外围控制器的性能。基于这种观察，本文提出了一种无模型的自适应迭代学习边界控制（MFAILPC）方案。该方法的三个特点是：（1）借助动态线性化数据建模技术，在控制器设计中不需要动力学模型，即它是一种数据驱动的方法，（2）在交通系统的重复操作模式的帮助下，周边控制器的性能将不断提高；（3）学习增益沿迭代轴进行自适应调整。与考虑建模误差，测量噪声，需求变化和时变MFD的多区域交通网络的各种控制方法进行比较，测试了该方案的有效性。仿真结果表明，与模型预测控制，比例积分控制等标准的周界控制方法相比，所提出的MFAILPC具有很大的潜力，并且对错误的恢复能力更强。与考虑建模误差，测量噪声，需求变化和时变MFD的多区域交通网络的各种控制方法进行比较，测试了该方案的有效性。仿真结果表明，与模型预测控制，比例积分控制等标准边界控制方法相比，所提出的MFAILPC具有很大的潜力，并且对错误具有更强的弹性。与考虑建模误差，测量噪声，需求变化和时变MFD的多区域交通网络的各种控制方法进行比较，测试了该方案的有效性。仿真结果表明，与模型预测控制，比例积分控制等标准边界控制方法相比，所提出的MFAILPC具有很大的潜力，并且对错误具有更强的弹性。