Adaptive driving condition path tracking control of intelligent vehicle
In order to solve the problem of vehicle tracking accuracy under different road adhesion coefficients and curvature conditions,a vehicle adaptive path tracking controller is proposed.Firstly,the model predictive control framework is established based on the three-degree-of-freedom vehicle dynamics model.The Dugoff tire model combined with the cubature Kalman filter algorithm is used to estimate the road adhesion coefficient,and the road adhesion coefficient and the optimal vehicle speed curve are fitted according to the vehicle operation stability evaluation index.According to different vehicle speeds and curvatures,the ant colony algorithm is used to optimize the optimal prediction time domain and control time domain under different working conditions,and a MPC controller with adaptive parameter time domain is designed.The simulation experiment is carried out in Carsim/Simulink.The results show that the MPC controller with adaptive parameter time domain will adopt appropriate time domain parameters under different working conditions.Compared with the traditional MPC controller,the maximum lateral deviation is reduced by 71%,the maximum yaw angle error is reduced by 84.5%,and the maximum sideslip angle is reduced by 23%.It can be seen that the adaptive time domain parameter controller designed in this paper is more stable and has better tracking effect.
path trackingmodel predictive controlestimation of road adhesion coefficientant colony algorithm
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