基于卡尔曼滤波迭代学习的交通信号控制方法

Traffic signal control method based on iterative learning of Kalman filter

由于城市交通流具有高度的复杂性,路网内存在的非重复性干扰会使迭代学习的交通控制系统动态性能变差。因此,提出了一种卡尔曼滤波器和迭代学习的交通信号复合控制方法,以进一步改善控制系统的鲁棒性和抗干扰能力。该控制方法首先利用卡尔曼滤波器对系统的状态进行观测,在含有随机噪声干扰的情况下,估计系统的最优状态;其次设计了带遗忘因子的迭代学习控制方法,遗忘因子可增强对大幅扰动的抗干扰能力,再通过迭代学习逐渐跟踪系统的参考轨迹;最后,对该算法的收敛性进行了数学证明,而仿真的实验结果也表明在扰动环境下利用提出的方法可以降低干扰对控制系统的影响,在一定程度上提高了道路通行能力、减少了交通拥堵。

Because of the high complexity of urban traffic flow,the non-repetitive interference in the road network will degrade the dynamic performance of the iterative learning traffic control system.Therefore,a hybrid control method based on Kalman filter and iterative learning is proposed to further improve the robustness and anti-interference ability of the control system.Firstly,the Kalman filter is used to observe the state of the system,and the optimal state of the system is estimated under the condition of random noise.Secondly,an iterative learning control method with forgetting factor is designed,which can enhance the anti-interference ability of large disturbance,and then the reference trajectory of the system is gradually tracked by iterative learning.Finally,the convergence of the algorithm is proved mathematically,and the simulation results also show that the proposed method can reduce the influence of interference on the control system in the disturbance environment,and improve the road capacity and reduce traffic congestion to a certain extent.