Research on Road Surface Identification Method for High Speed Electric Drive Tracked Vehicles Based on Traction Slip Rate Characteristics
Electrically driven high-speed tracked vehicles have large mass, high speed and complex driving conditions. There is great significance to identify the ground characteristics for its dynamic control. Based on the traction-slip characteristics between electrically driven high-speed tracked vehicles and the ground, this paper proposes a ground recognition method using long short-term memory neural network ( LSTM) . The vehicle longitudinal and lateral acceleration, drive force, rotation speed are obtained by collecting the driving motor controller and inertial measurement unit during the driving process. The traction characteristics of the vehicle in a sliding change process are recorded and screened, which are highly correlated with the ground characteristics. Through the LSTM neural network, the feature data are classified to identify the current ground characteristics. This method has the advantages of simple acquisition equipment, easy access to signals, and low computility requirements. Simulation results show that the method can accurately classify the vehicle driving state into three typical adhesion surfaces:high, medium and low.
track slidinglong short-term memory neural networkgroundon line ground identification
万方数据
维普
