Unknown Input‐Compensated Least Mean Square Adaptive Filtering for Active Torsional Vibration Suppression in Planetary Hybrid Drivetrain Systems

Abstract

The multi‐speed planetary hybrid drivetrain features a complex structure, where transient variations from multiple excitation sources are highly coupled with vehicle structural parameters, leading to severe torsional vibration issues, especially during engine start‐stop processes. This study proposes an unknown input‐compensated least mean square (UIC‐LMS) adaptive algorithm for active torsional vibration suppression. An engine excitation model is established through time‐ and frequency‐domain analyses, while a six‐degree‐of‐freedom dynamics model of the drivetrain is developed using the lumped mass method, with frequency‐domain analysis revealing the resonance mechanisms. To achieve precise torque estimation under uncertainties, a reduced‐order unknown input observer (UIO) is designed by reconstructing measured outputs. The least mean square (LMS) adaptive filtering then computes motor compensation torque, with real‐time parameter adjustment via a transversal filter. Hardware‐in‐the‐loop test demonstrate that the UIO improves estimation accuracy by over 78.9% compared to the Luenberger observer, showing superior robustness against unknown input disturbances and measurement noise. Under UIC‐LMS control, maximum amplitude fluctuations of transmitted torque and torsional angle in the torsional vibration damper are reduced by over 44.8%, while those in the driveshaft are reduced by more than 72.5%. Drawing parallels with advanced equivalent‐input‐disturbance rejection strategies for uncertain systems, the algorithm maintains superior effectiveness across drivetrain parameter uncertainties by integrating UIO‐based lumped disturbance observation with LMS adaptive compensation. Real‐vehicle validation confirms the algorithm's robustness and high torsional vibration suppression rates in embedded controllers.