Research On Trajectory Tracking Of Distributed Driving Intelligent Vehicle Based On Model Prediction Control

Electricity and intelligence are the development direction of automobiles in the future and trajectory tracking control is one of the key technologies to achieve intelligence.The research topic of this thesis is that how to achieve trajectory tracking control by controlling the front wheel steering and the driving / braking force of four wheels for distributed driving electric vehicles.Distributed drive electric intelligent vehicle have the characteristics of strong coupling,strong non-linearity,and overdrive.Considering that Model Predictive Control(MPC)can handle multi-constrained multi-coupling problems,a trajectory tracking controller is designed based on MPC.The main research contents include:Pacejka 5.2 tire model is built to describe the mechanical characteristics of the tire and a seven-degree-of-freedom vehicle model is established through reasonable assumptions.The trajectory tracking controller is designed based on the nonlinear vehicle model which guarantees the accuracy of the model on the one hand and reduces the computational complexity on the other hand.According to Carsim modeling requirements,the corresponding vehicle and related subsystem experiments are performed and the experimental data is processed to establish a Carsim vehicle model as a control object.The simulation results are compared with the experiments under certain operating conditions.The nonlinear system is transformed into a linear system through local linearization and model predictive control algorithm is derived based on the linear system which is transformed into a quadratic programming problem for computer to solve.A non-linear vehicle model is used to design the MPC trajectory tracking controller,the steering angle of the front wheel and the tire force of the four wheels are used as control variable,tire side slip angle is used as soft constraint control variable to ensure the stability of the vehicle.The effectiveness of the controller is verified by comparing it with the Preview Driver Model(PDM)on different vehicle speeds and adhesion roads.The MPC controller has better overall performance and is more robust to the road adhesion coefficient.A hierarchical controller is designed to decouple the longitudinal and lateral movement of the vehicle.In the upper controller,the lateral control uses MPC to calculate the front wheel angle and the total yaw moment,the longitudinal control uses Sliding Mode Control to calculate the total driving force.The lower controller optimizes the total driving force and yaw moment to the actuators of the four wheels with a certain objective function.The comparison with the MPC controller on different speeds and adhesion roads verifies the effectiveness of the hierarchical controller.