Adaptive unscented Kaiman filter with a fuzzy supervisor for electrified drive train tractors

Electrified drive trains for tractors are supposed to realize great potential of raising performance in heavy operations via optimal traction control. The paper proposes to apply an adaptive unscented Kaiman filter (UKF) with a fuzzy supervisor for identification of electrical drive train tractor dynamics. The key advantage of electrical drive trains lies in feedback of drive torque which plays crucial role in traction parameter estimation. It is known that without using special adaptation techniques, an UKF may cause some divergence problems and lowered precision of estimation as well as its predecessor, an extended Kaiman filter (EKF). A method based on a fuzzy logic supervisor in addition to adaptation of an UKF is proposed to maintain trade-off between tracking strength and estimation accuracy. Simulation results with a comprehensive tractor dynamics model showed increase in estimation precision of traction parameters. Laboratory experiments using a test stand with an electrical load machine showed appropriate estimation of the load torque.