Computationally Efficient Indirect Kalman Filter for Hydraulic Machinery

EasyChair Preprint 13475

Abstract

Hydraulic machinery such as cranes and excavators are multidisciplinary systems that combine mechanics, hydraulics, and electronics. Their computer simulation often includes data fusion from real sensors using various information fusing techniques such as state and parameter estimations. Out of various methods, the error-state extended Kalman filter, also known as the indirect Kalman filter, is often used in the literature because of their high computational efficiency for multibody systems. However, their application to hydraulically actuated multibody systems utilizes a numerical approach and consequently, it affects to the computational efficiency.

The objective of this study is to introduce an efficient indirect Kalman filter that utilizes a semi-analytical approach in computing the state-transition matrix in the framework of hydraulic machinery. The introduced methodology is compared with the numerical approach available in the literature. As a case example, a hydraulic crane is considered, where the mechanics is modeled using the index-3 augmented Lagrangian-based semi-recursive formulation and the hydraulics is modeled using the classical lumped fluid theory. The two filters are compared based on the accuracy of the state estimations and the associated computational efficiencies.

Keyphrases: Multibody dynamics, Nonlinear Kalman filter, hydraulic actuator, hydraulic crane

@booklet{EasyChair:13475,
  author    = {Suraj Jaiswal and Aki Mikkola},
  title     = {Computationally Efficient Indirect Kalman Filter for Hydraulic Machinery},
  howpublished = {EasyChair Preprint 13475},
  year      = {EasyChair, 2024}}