Load Weight Estimation on Excavators
Ferlibas, Mehmet (2020)
2020
Master's Programme in Automation Engineering
Tekniikan ja luonnontieteiden tiedekunta - Faculty of Engineering and Natural Sciences
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Hyväksymispäivämäärä
2020-10-26
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202010237442
https://urn.fi/URN:NBN:fi:tuni-202010237442
Tiivistelmä
Excavators are widely used in earth moving operations. There is a need for payload monitoring systems on these hydraulically operating machines in order to prevent the possible problems while transferring the material, increase the efficiency, and obtain the product information automatically.
This research proposes a method for estimating the load weight in the excavator’s bucket. The problem was separated to two parts as static estimation and dynamic estimation of the load weight. The collected data is processed offline and a parameter estimation process was performed in both of the static estimation and dynamic estimation by using least squares estimation of parameters to predict the no load torque values. There was a need to estimate the angular velocities and angular accelerations from the angular position measurements in order to utilize the method in dynamic estimation. The angular velocity measurements are used as reference to observe the accuracy of the angular velocity estimations. The friction is neglected in modeling throughout the work. However, the effect of the static friction is obviously present in the collected data.
The method was tested with two different load weights in each of the static estimation and the dynamic estimation. The results obtained showed that the static friction plays an important role in static estimation. However, the developed method provides accurate enough results that the error in dynamic load weight estimation is less than 2% for high enough velocities. Finally, further improvements are suggested in the end of this work.
This research proposes a method for estimating the load weight in the excavator’s bucket. The problem was separated to two parts as static estimation and dynamic estimation of the load weight. The collected data is processed offline and a parameter estimation process was performed in both of the static estimation and dynamic estimation by using least squares estimation of parameters to predict the no load torque values. There was a need to estimate the angular velocities and angular accelerations from the angular position measurements in order to utilize the method in dynamic estimation. The angular velocity measurements are used as reference to observe the accuracy of the angular velocity estimations. The friction is neglected in modeling throughout the work. However, the effect of the static friction is obviously present in the collected data.
The method was tested with two different load weights in each of the static estimation and the dynamic estimation. The results obtained showed that the static friction plays an important role in static estimation. However, the developed method provides accurate enough results that the error in dynamic load weight estimation is less than 2% for high enough velocities. Finally, further improvements are suggested in the end of this work.