Markovian filtering for driveshaft torsion estimation in heavy vehicles

摘要

Even though driveshaft torsion is a fundamental variable in vehicle dynamics, it is difficult to be measured or estimated due to the need for high precision encoders or because of integration estimation errors. Furthermore, gear shifting in the driveline affects driveshaft torsion estimation, as it abruptly changes powertrain dynamics. Another issue in driveshaft torsion estimation is the influence of the road slope, which disturbs the system, and may or may not be measured. This paper proposes the estimation of driveshaft torsion by a robust filter for discrete-time Markov jump linear systems, with or without road slope information. This algorithm is tested for a truck bodywork. Results show that the estimation delivers online results as accurate as offline estimation methods, especially when the road slope is known. The proposed filter is capable of estimating the torsion even in scenarios of high plant uncertainty, where an LMI-based filter cannot find a feasible solution.