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.
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Recommended by Associate Editor Young Soo Suh under the direction of Editor Duk-Sun Shim.
Kwang-seok Oh received his B.S. degree in mechanical engineering from Hanyang University in 2009 and his M.S. degree in mechanical and aerospace engineering from Seoul National University, Korea in 2013. He is a Professor in the Department of Mechanical Engineering at Hankyong National University, Korea. His research interests include autonomous vehicle, fault-tolerant control, safey control, and driver modeling.
Ja-ho Seo received his B.S degree in agricultural machinery and process engineering from Seoul National University, Seoul, Korea in 1999, his M.E degree in mechanical engineering from the University of Quebec (Ecole de Technologie Superieure), Montreal, Canada in 2006, and is Ph.D. in mechanical engineering from the University of Waterloo,Waterloo, Canada in 2011. He was with the Department of Mechanical and Mechatronics Engineering of the University of Waterloo as a postdoctoral fellow in 2011, the Department of System Reliability of the Korea Institute of Machinery & Materials (KIMM) as a senior researcher for 2012–2016, and the Department of Biosystems Machinery Engineering of Chungnam National University, Korea as an assistant professor for 2016–2017. Since 2017, he has been an assistant professor at the Department of Automotive, Mechanical and Manufacturing Engineering, University of Ontario Institute of Technology where he has been involved in research on the development of autonomous control systems for intelligent mobile machines.
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Oh, Ks., Seo, Jh. Inertial Parameter Estimation of an Excavator with Adaptive Updating Rule Using Performance Analysis of Kalman Filter. Int. J. Control Autom. Syst. 16, 1226–1238 (2018). https://doi.org/10.1007/s12555-017-0087-1
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DOI: https://doi.org/10.1007/s12555-017-0087-1