Research On Trajectory Tracking Control Based On Improved ACO Algorithm

In recent years,with people's deepening research on automobile safety,autonomous driving technology has become the focus of the automobile industry.Automated driving can reduce the dependence of vehicles on drivers,ease traffic congestion,and improve driving safety and traffic orderliness.Path planning and trajectory tracking are one of the core technologies in automatic driving.It is very important to realize trajectory planning and control smoothly and accurately for automatic driving.In this paper,aiming at the problem of track tracking control,considering the main factors to make reasonable assumptions on the vehicle model,the seven degree of freedom vehicle model and steering geometry model are established,and the magic formula is selected to calculate and analyze the lateral and longitudinal forces of the tire.In view of the problems of swarm intelligence path planning algorithm,it is difficult to meet the vehicle stability conditions,the real-time planning environment does not meet the real-time requirements,and the complex environment is easy to fall into the local optimal solution.Based on the research at home and abroad,the main influencing factors were researched and analyzed.The teleportation factor was introduced into the state transition formula and the initial pheromone concentration and the updating method were improved.Considering the operational stability of the vehicle during driving,the planned original path is smoothly processed,and a coefficient that can be adjusted according to the specific driving scenario of the vehicle is added.The improved ACO algorithm is combined with the trajectory tracking controller for simulation.The test results show that the algorithm can be effectively applied to the path planning of intelligent vehicles.Aiming at the problems of non-linear,time-varying,and uncertain characteristics of the vehicle model,which lead to low trajectory tracking control accuracy,vehicle robustness,and poor steering stability,an adaptive model predictive control method with variable parameters is adopted.In order to compare and analyze the performance evaluation indexes of the model predictive control method designed in this paper,the optimal control model and the SMC control model were studied in terms of nonlinear control and linear control.Design the sliding mode surface with the heading angle error model and the center of mass side deviation angle.For the chatter phenomenon existing in the SMC control,the exponential approach rate is used to replace the signfunction in the traditional algorithm with the saturation function;To adapt to the adjustment,the control increment is used to replace the cost function of the control quantity,and the relaxation factor is introduced to complete the design of the MPC trajectory tracking controller.The simulation results show that the control method designed in this paper has strong anti-interference ability,which verifies the effectiveness and robustness of the control system.