Kalman Filter for Integrated Kinematics of Multibody Systems and Hydraulic Systems

EasyChair Preprint 13382

Abstract

Monitoring and control of advanced mechatronic systems requires precise data on the current state of the system. This often involves equipping the systems with appropriate sensors. However, measuring all variables is not always economically or technically viable, and their values must be computationally determined. State estimation is a method that merges prior knowledge of system behavior (the model) with observed behavior (measurements) to infer deeper system insights (virtual measurements). Within multibody systems, this prior knowledge typically includes the equations of motion of the system. However, these equations can sometimes be significantly offset by factors like unknown contact forces or undetermined mass properties. In such scenarios, the kinematic Kalman filter emerges as a robust alternative. This technique disregards the equations of motion as prior knowledge and instead utilizes acceleration data to drive the estimation. This work presents the key findings of the recently published work by the authors, where the kinematic Kalman filter approach is extended by substituting acceleration data with hydraulic pressure measurements and leveraging the established relationship between kinematics and hydraulic pressures.

Keyphrases: Discrete Extended Kalman filter, hydraulics, state estimation

@booklet{EasyChair:13382,
  author    = {Lauri Pyrhönen and Suraj Jaiswal and Aki Mikkola},
  title     = {Kalman Filter for Integrated Kinematics of Multibody Systems and Hydraulic Systems},
  howpublished = {EasyChair Preprint 13382},
  year      = {EasyChair, 2024}}