In order to understand the situation of enterprise vehicles,more accurately grasp the car trajectory,this paper studies the enterprise car trajectory tracking model based on MPC control and improved limit learning machine.It collects and prepro-cesses the basic information of vehicle driving;optimizes and improves ELM model using particle group optimization algorithm,outputs vehicle driving information with different advantages through DNN model and the improved ELM model.It fuses dif-ferent characteristic vehicle driving information using fusion layer,and the output layer is used to output the horizontal and lon-gitudinal predicted position of the vehicle.The MPC model controller is used to linearize and discretize the vehicle prediction position.The time domain input sequence is calculated during the vehicle tracking cycle using the model prediction control algo-rithm,and the first element within the sequence is used as the control input to realize the vehicle track tracking after repeated cycles.The experimental results show that the mean square error fluctuation range of the model is 0.006～0.013 with good generalization ability;the tracking accuracy is up to 100％,and has strong reliability.
维普
万方数据