Road adhesion coefficient estimation based on improved extreme learning machine
The road adhesion coefficient is one of the most critical parameters in the interaction between vehicle and road.Accurately identifying the road adhesion coefficient can be used to determine the optimal safety control mode for vehicles.Therefore,a method for estimating the road adhesion coefficient based on an improved extreme learning machine(ELM)was proposed.First,the dynamic analysis of the vehicle was conducted to determine the input variables of the neural network model.Then,the complete vehicle model and operating conditions were set up to establish a dataset through simulation experiments.Next,the dataset was processed using a clipping recursive averaging filter algorithm,and the ELM was improved and optimized using the sparrow search algorithm to enhance the accuracy and stability of the ELM.Finally,experimental results showed that the improved ELM had a comprehensive improvement in performance,with a prediction accuracy of 93.4%,an increase of 4.89%,and a 41.33%increase in convergence speed.
extreme learning machineestimation of road adhesion coefficientsparrow search algorithmneural network
NETL
NSTL
万方数据
