基于PSO-SMO的分布式驱动车辆轮胎力级联估计OA

To address the limitations of traditional tire model-based tire force observer which relies on accurate tire model and road adhesion coefficient,this paper proposes a tire force cascade estimator based on particle swarm optimization-sliding mode observer(PSO-SMO).First,a vehicle load transfer model is built to estimate the vertical force of the tire by considering the centroid deviation and suspension movement of the vehicle.Meanwhile,the tire longitudinal force estimator is designed based on the wheel dynamics model and PSO-SMO estimation algorithm.Then,the estimation of longitudinal and vertical force is treated as known inputs,and combined with the parameters such as front wheel angle and yaw velocity,the lateral force estimation is realized based on the PSO-SMO estimation algorithm.Finally,the simulation experiments are conducted using the Carsim-Simulink co-simulation platform.Results demonstrate the proposed estimator effectively estimates the tire force under various driving conditions,exhibiting faster convergence speed and higher estimation accuracy than the traditional observer.In particular,compared with the traditional volumetric Kalman filter scheme,the proposed method exhibits stronger robustness under road conditions with varied adhesion coefficients.