Tracked Vehicle Velocity Estimation by Disturbance Observer and Machine Learning, and its Application to Driving Force Control for Slippage Suppression

Abstract

Tracked vehicles generally involve slippage owing to the interaction between the road and track surfaces, which renders accurate motion control difficult. This paper proposes a velocity estimation method for a tracked vehicle with slippage, and its application to driving force control. In this method, the disturbance estimated by a disturbance observer was used as information related to slippage, and a neural network was constructed for velocity estimation. In addition, a driving force observer was designed using the estimated velocity. The driving control of the tracked vehicle to suppress slippage was achieved by using the feedback of the estimated driving force. The proposed method was evaluated experimentally through the velocity estimation performance and slip suppression performance tests.