Vehicle Path Tracking Preview Control Based On Model Prediction Method Under Constraints

With the rapid development of social economy and technology,autonomous vehicles have entered the public’s vision.The development of autonomous vehicles provides important solutions to problems such as traffic congestion and energy crises.Among them,as the key of autonomous driving technology,the path tracking control of autonomous vehicles has put forward higher and higher requirements for vehicle tracking ability and driving stability.In order to adapt to more diverse application scenarios,vehicle tracking control algorithms need to consider more actual environmental factors and vehicle state,which has become a research hotspot for experts and scholars.Therefore,in order to improve the tracking performance and stability of the controller,this thesis focuses on the tracking control of autonomous vehicles and researches the path tracking control method.The main research contents are as follows:(1)In order to study the path tracking problem of autonomous vehicles under constrained conditions,we introduce the theory of model predictive control and design the control strategy of vehicle path tracking under constrained conditions.The frontwheel steering path tracking controller is designed based on the vehicle dynamics model,and the constraint conditions are designed according to the actual conditions.The objective function is solved by online rolling optimization,and the optimal control law is applied to the controlled vehicle platform,so as to realize the path tracking of autonomous vehicles.Finally,the simulation verifies the feasibility of the controller for path tracking,and initially achieves the purpose of autonomous vehicles for desired path tracking.(2)In order to further improve the tracking accuracy and system response speed,based on the characteristics of forward-looking vehicles during actual driving,a preview tracking strategy based on model predictive control was proposed.First,under the scenario that the road ahead information is available,an augmented error system is constructed based on the prediction tracking error,state difference and preview information.By introducing an appropriate objective function,the original tracking control problem is transformed into an optimal control problem.The optimal control sequence is obtained by the principle of minimum value.In addition,the objective optimization problem under constraints is considered,and the stability of the closedloop system is proved by adopting Lyapunov’s second method.Finally,the platform of Car Sim and Simulink is adopted to verify the effectiveness of the path preview tracking.The results show that the preview tracking strategy based on model predictive control has better tracking effect.(3)Considering the influence of external disturbances on the stability of the controller in the actual driving environment,the idea of sliding mode control is introduced on the basis of the previous study.In this thesis,a preview tracking strategy based on sliding mode predictive control is proposed.Firstly,the sliding mode surface is designed according to the error between the actual output and the reference trajectory of the system,and the discrete sliding mode prediction model is established by the idea of prediction function.Then,an augmented system containing the output of sliding mode prediction,state difference and preview information is constructed.By combining the sliding mode approach law,the optimal performance index is designed.Finally,it is demonstrated that the closed-loop system with sliding mode prediction in the disturbance environment is robust and stable.Through simulation and comparison experiments,the effectiveness of this strategy in the interference environment is verified,which makes the autonomous vehicle have better driving stability,and maintains good tracking effect while restraining the interference influence.Through simulation and comparison experiments,the effectiveness of this strategy in the interference environment is verified,which makes the autonomous vehicles have better driving stability,and maintains good tracking effect while restraining the interference influence.