Trajectory Tracking Control with Estimated Driving Force for Tracked Vehicle Using Disturbance Observer and Machine Learning

This paper proposes a tracking control method that suppresses slippage by using the driving force of a tracked vehicle. First, the velocity of the tracked vehicle including slippage is estimated using a disturbance observer and machine learning technique. This estimated velocity is utilized to design an observer that can estimate the driving force of the crawler. By distributing and controlling the driving force, tracking control with reduced slippage can be realized. The experimental results demonstrate the tracking performance of the proposed control system.