Research on fuzzy adaptive trajectory tracking control based on road adhesion coefficient estimation
To improve the control accuracy and stability of intelligent vehicle trajectory tracking in complex working conditions,this paper proposes a fuzzy adaptive trajectory tracking control strategy that adapts to sudden changes in road adhesion coefficient and dynamic changes in vehicle speed.A seven degree of freedom vehicle dynamics model is built and a vehicle trajectory tracking controller is designed based on Model Predictive Control (MPC )algorithm.The relationship between MPC controller parameters,road adhesion coefficient,vehicle speed,and vehicle trajectory tracking performance is investigated.To obtain the road surface adhesion coefficient of the current road in real-time,an Unscented Kalman Filter estimator is designed based on the Dugoff tire model and the vehicle dynamics model.Based on this,an MPC controller parameter adaptive fuzzy controller is designed with road adhesion coefficient and vehicle speed as input variables,which adjust the parameters of the MPC controller in real time to improve the stability and tracking ability of vehicles in complex working conditions.The designed control method is validated through simulation experiments.Our results show the proposed control method ensures vehicle stability under low-speed conditions and improves vehicle tracking accuracy.Under medium speed conditions,it effectively addresses low trajectory tracking accuracy and poor stability on roads with sudden changes in road adhesion coefficient.
trajectory trackingestimation of road adhesion coefficientmodel predictive controlfuzzy control
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