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Inertial Parameter Estimation of an Excavator with Adaptive Updating Rule Using Performance Analysis of Kalman Filter

  • Kwang-seok Oh
  • Ja-ho Seo
Regular Papers Control Theory and Applications
  • 42 Downloads

Abstract

This paper presents a rotational inertia estimation algorithm for excavators based on recursive least-squares with forgetting and an adaptive updating rule that uses the performance analysis of the Kalman filter. Generally, excavators execute a swing motion with various materials, and the rotational inertia of the excavator is changed greatly due to the excavator’s working posture. The large variation in the rotational inertia of the excavator has an influence on the dynamic behaviors of the excavator, and an estimation of the excavator’s rotational inertia is essential to developing a safety system based on prediction of dynamic behavior. Therefore, a real-time rotational inertia estimation algorithm has been proposed in this study using a swing dynamic model. The proposed estimation algorithm has been designed using only swing velocity, utilizing the recursive least squares method with multiple forgetting for practical application to actual excavators. Two updating rules have been applied to the estimation algorithm in order to enhance the estimation performance. The first proposed rule is the damping coefficient updating rule. The second rule is the forgetting factor updating rule based on real-time analysis of linear Kalman filter estimation performance. The performance evaluation of the estimation algorithm proposed in this paper has been conducted based on the excavator’s typical dumping scenario. The performance evaluation results show that the developed inertia estimation algorithm can estimate actual rotational inertia with the two designed updating rules using only excavator swing velocity.

Keywords

Dumping scenario forgetting factor Kalman filter recursive least squares (RLS) rotating inertia updating rule 

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Copyright information

© Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringHankyong National UniversityGyeonggi-doKorea
  2. 2.Department of Automotive, Mechanical and Manufacturing EngineeringUniversity of Ontario Institute of TechnologyOshawaCanada

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