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http://dx.doi.org/10.7839/ksfc.2016.13.4.059

Model-Prediction-based Collision-Avoidance Algorithm for Excavators Using the RLS Estimation of Rotational Inertia  

Oh, Kwang Seok (Department of Automotive Engineering, Honam University)
Seo, Jaho (Department of Biosystems Machinery Engineering, Chungnam National University)
Lee, Geun Ho (Korea Institute of Machinery & Materials)
Publication Information
Journal of Drive and Control / v.13, no.4, 2016 , pp. 59-67 More about this Journal
Abstract
This paper proposes a model-prediction-based collision-avoidance algorithm for excavators for which the recursive-least-squares (RLS) estimation of the excavator's rotational inertia is used. To estimate the rotational inertia of the excavator, the RLS estimation with multiple forgetting and two updating rules for the nominal parameter and the forgetting factors was conducted based on the excavator-swing dynamics. The average value of the estimated rotational inertia that is for the minimizing effects of the estimation error was computed using the recursive-average method with forgetting. Based on the swing dynamics, the computed average of the rotational inertia, the damping coefficient for braking, and the excavator's braking angle were predicted, and the predicted braking angle was compared with the detected-object angle for a safety evaluation. The safety level defined in this study consists of the three levels safe, warning, and emergency braking. The analytical rotational-inertia-based performance evaluation of the designed estimation algorithm was conducted using a typical working scenario. The results of the safety evaluation show that the predictive safety-evaluation algorithm of the proposed model can evaluate the safety level of the excavator during its operation.
Keywords
Model predictive safety evaluation; Recursive least square; Forgetting factor; Updating rule; Swing dynamics;
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