基于模型预测控制的快速路网匝道调节方法

Expressway ramp metering strategy based on model predictive control

探索了基于模型预测控制(MPC)的匝道调节方法.提出了匝道MPC调节的非线性动态时间离散最优控制模型及其解法.最优控制模型采用动态网络交通流模型作为过程模型,采用遗传算法求解.考察了匝道MPC调节的效果和鲁棒性,并将其效果与经典的ALINEA匝道调节方法相比.针对三起点三终点快速路网的仿真案例显示,匝道MPC调节能明显缓解拥堵,改善路网总体运行效率,较之ALINEA调节能够更连续平稳地调节交通流,在存在预测误差的情况下控制效果依然很好,其路网总耗时改善率明显高于ALINEA调节,具有很好的鲁棒性和应用前景.

The model predictive control（MPC） based ramp metering strategy was explored,in which online non-linear optimization was applied.The dynamic non-linear time-discrete optimal control model was established and the associated solving algorithm was presented.In the optimal control,a dynamic expressway network traffic flow model was adopted as the process model,and a genetic algorithm was applied to solve the optimization problem.By a simulated case study the efficiency and robustness of the MPC strategy was tested.The results show that,the MPC strategy can obviously alleviate congestion and improve the overall network performance.It can control vehicle flow more smoothly comparing to the widely used ALINEA feedback control strategy.The performance criterion of TTS（total time spent） is 1.2% higher,thus it is of good control robustness.