Research On The Trajectory Tracking And Obstacle Avoidance Control Of Intelligent Vehicle Based On Model Predictive Control

With the development of intelligent driving technology,intelligent vehicle trajectory tracking control technology is becoming more and more important.In this study,relying on National Key R&D Program of China,research on intelligent vehicle trajectory tracking and obstacle avoidance control is studied.Aiming at the problem that the tracking accuracy and stability of the intelligent vehicle’s trajectory decline sharply,a linear time-varying model predictive control method is used to establish soft constraints on tire slip angle and yaw stability to ensure that the tire is within the linear stability range,thereby improving the accuracy and stability of trajectory tracking.Considering the large amount of calculation of model predictive control and low real-time performance,an event-triggered model predictive controller is proposed,which allows the remaining elements of the control variables in the control horizon to be applied to the system once a specific condition is satisfied.The proposed method offers greatly improved real-time performance while the tracking accuracy is guaranteed.Aim at the problem that the controller often fails when intelligent vehicles encounter obstacles in the real condition,a simultaneous trajectory tracking and obstacle avoidance control algorithm for intelligent vehicle based on model predictive control is proposed to improve the adaptability of the trajectory tracking controller in complex environments.The main research contents are as follows:(1)The 7DOF vehicle dynamics model is constructed.Aiming at the problem that nonlinear model predictive control often fall into local optimum,the linear time-varying model predictive control is used to establish the trajectory tracking controller for intelligent vehicle.In order to achieve efficient calculation,the barrier method is introduced for numerical solution.Simulations based on Matlab/Simulink and Carsim show that compared with other controllers,model predictive control has significant advantages,but it will lose control at a high speed.(2)In order to address the problem of model predictive control at a high speed,soft constraints of tire sideslip angle and yaw stability are established owing to the characteristics that model predictive control can be conveniently used to handle constraints.Simulations based on Matlab/Simulink and Carsim show that after introducing constraints,the performance of the controller has been significantly improved in aspect of control accuracy or stability.(3)In the reason of that MPC needs to online calculate the quadratic programing problem,the huge amount of calculation may affect the controller’s real-time performance.By introducing an event-trigger mechanism,the remaining elements of the control variables in the control horizon are applied to the system once a specific condition is satisfied.Simulations based on Matlab/Simulink and Carsim show that the proposed event-triggered model predictive controller can not only ensure the tracking accuracy,but also save the calculation of the controller to a large extent,thereby improving the real-time performance of the system.(4)In order to address the problem of the failure of a single trajectory tracking controller after an obstacle occurs,a simultaneous trajectory tracking and obstacle avoidance control algorithm for intelligent vehicle based on model predictive control is proposed.Aim at the problem that the controller often fails when intelligent vehicles encounter obstacles in the real condition,an obstacle avoidance strategy is given based on the safety distance model.A cubic polynomial with the information of road lane line and obstacles is used to describe the obstacle avoidance trajectory.The prediction model is used to derive the reference trajectory in the future predictive horizon,thereby constructing the model predictive control cost function,and converting the obstacle avoidance control problem into a quadratic programming problem.Simulations based on Matlab/Simulink and Carsim show that the proposed simultaneous trajectory tracking and obstacle avoidance control algorithm based on model predictive control can realize the safe and comfortable lane changing and obstacle avoidance.(5)Based on ‘ARRIZO 5E’ intelligent driving platform,the designed simultaneous trajectory tracking and obstacle avoidance control algorithm vehicle based on model predictive control is deployed in ROS.Under the condition of actual road,the experiment of trajectory tracking and obstacle avoidance control is carried out at a mid-high speed.The experimental results show the feasibility and reliability of the simultaneous trajectory tracking and obstacle avoidance control algorithm based on model predictive control proposed in this paper.