Research On Multi-objective Optimal Control Strategy For Stop-and-go Cruise In Urban Conditions

The vehicle stop-and-go cruise control system for complex urban traffic conditions is a significant part of the advanced driver assistance system,and has received extensive attention from domestic and foreign car companies and research institutions in recent years.By controlling the speed of the car,the stop-and-go cruise control system can effectively reduce the driver’s operational burden caused by frequent starting and stopping in urban traffic,and improve driving safety and road capacity.Aiming at the problem that the current system emphasizes more on the safety and followability during driving,while ignoring the driver’s feeling,this paper is based on the hierarchical control structure,and also considers the driver’s comfort and the vehicle’s fuel economy.Four contradictory optimization objectives were established,and the multi-objective optimization control method of stopand-go cruise control system was studied.The main research contents are as follows:Firstly,the system of the upper decision-making layer and the lower control layer under the hierarchical control framework is divided,and the control objectives are analyzed,and a number of subsystems with clear relationships are determined.The existing safe distance strategies are analyzed and summarized,and a desired safe distance model that is more in line with the driver’s control is designed.Aiming at the traffic scene applied by the stop-andgo cruise system,it is divided into speed control and distance control,the switching logic between speed control and distance control is designed,and a state machine model is established.Secondly,the vehicle dynamics model is established using Carsim.Based on the vehicle driving resistance model,the inverse longitudinal dynamics models of the driving/braking conditions are established respectively,and the switching strategy including the transition interval of the working conditions is designed.Thirdly,due to the time-varying and nonlinear characteristics of the vehicle inverse longitudinal dynamics model,the pure feedforward control cannot achieve the control accuracy of the lower control layer.Therefore,based on the inverse longitudinal dynamics model,a lower control layer algorithm with feedforward plus fuzzy PID feedback is established to better adapt to complex urban traffic conditions.With four different expected acceleration signals as input,the built lower control layer model is verified.Then,the structure of the upper decision-making layer is analyzed,and the speed controller and the distance controller are designed.Under the framework of model predictive control,the safety,followability,fuel economy and driver comfort of the vehicle during driving are analyzed.Based on the mutual longitudinal dynamics model of the workshop,the four contradictory optimization goals are transformed into performance.Based on the indicators and system constraints,a multi-objective optimal MPC distance control strategy is established.At the same time,a PID control method is used to establish a speed control strategy.Finally,Matlab/Simulink and Carsim are used to establish a co-simulation platform for the stop-and-go cruise control system.Seven typical types are selected including cruise control at low speed,follow-through at low speed,cut-in from the preceding vehicle,cutout from the preceding vehicle,follow-to-stop,follow-to-start and stop-and-go cruise conditions are simulated.The results show that the designed control algorithm can have a good control effect in each working condition.