A Kalman estimator for detecting repetitive disturbances

200530 citationsJournal Article

Authors

Liang Chen · University of Shanghai for Science and Technology

Paolo Mercorelli · Fraunhofer Project Centre Wolfsburg

Steven Liu · University of Kaiserslautern

Abstract

The paper proposes a two-stage Kalman filter combining a robust and optimal algorithm for state and disturbance estimation. A practical case coming from automotive application is studied in detail to show the effectiveness of the proposed technique.

Topics & Keywords

Iterative Learning Control Systems Control Systems and Identification Hydraulic and Pneumatic Systems

UN Sustainable Development Goals

Affordable and clean energy

Publication Details

DOI: 10.1109/acc.2005.1470201

Field-Weighted Citation Impact: 2.87