| Low-carbon,intelligent and networked are the three hot issues in automotive technology research,and the development of intelligent networked vehicles is the future development trend of the automotive industry.The development of public transport is one of the main ways to alleviate the current urban traffic congestion.The development of smart buses is conducive to reducing traffic accidents,improving road safety operation level,reducing safety hazards,and improving the protection of passengers,pedestrians and other traffic participants.Path tracking is one of the key technologies of intelligent vehicles.This paper takes zhongtong bus LCK6668 EVG as the research object,and conducts a path tracking research on the characteristics of the bus.The main research contents are as follows.The simulation model is built on the basis of real vehicle and the relevant verification is carried out.According to the vehicle parameters of LCK6668 EVG,the vehicle model was built in Truck Sim,and the control stability simulation was carried out.At the same time,the prediction model of vehicle two-degree of freedom dynamic equation of state was established as the controller.The model was tracked and compared with the vehicle model established in Truck Sim to show that the prediction model could better reflect the vehicle driving characteristics.Finally,the road model is established by using the function equation of the position of the known road points,and the reference path and the reference yaw Angle are used as the reference input of the controller.In order to realize the path tracking of intelligent bus,a model prediction controller is designed.The vehicle state equation of two degrees of freedom(dof)is used as the control model,and it is linearized and discretized.The linear quadratic objective function is established,and the constraint conditions are added to the objective function according to the actual physical limit conditions.The element in the control sequence obtained is used as the control input of the controlled object,and the rolling optimization is carried out through repeated iteration.Using MATLAB to write M function file control strategy.In order to verify the control strategy based on model prediction,the trucksim-matlab/Simulink co-simulation platform was established.Firstly,the model prediction controller was embedded into the simulation platform,and a control test was set to compare the path tracking control based on the driver prediction model which has been used in Truck Sim.The simulation results show that the tracking effect of the model prediction controller is better than that of the model prediction tracking control.According to the characteristics of the load variation of the bus,the tracking accuracy and driving stability of the model predictive controller under different mass are studied.The simulation results show that the quality variation affects the tracking accuracy and driving stability of the controller.Aiming at the above problems,a quality update model is proposed,and the model prediction controller is optimized.In order to further verify the optimized control strategy,an intelligent vehicle hardwarein-loop test platform was established.The man-machine interaction interface is developed in the Lab View software on the PC of the upper computer.The control algorithm generates dynamic link file(DLL)in the Matlab/Simulink environment and is embedded in the PXI of the lower computer as the controller.PXI establishes contact with the lower execution parts through CAN communication,and outputs control quantity to control the steering motor.Meanwhile,it receives the execution information of the execution parts and displays it in the human-machine interaction interface.The experimental results show that the tracking ability of the model predictive controller is better than that of the preview control,and the tracking accuracy and running stability of the model predictive control with quality update is better than that of the controller without quality update. |
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