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Horrian, S., Bahrami, S. (2020). Estimation of the Payload mass in DELTA robot by using the motor torque. Electronic and Cyber Defense, 16(2), 27-38.
Siavash Horrian; Saeed Bahrami. "Estimation of the Payload mass in DELTA robot by using the motor torque". Electronic and Cyber Defense, 16, 2, 2020, 27-38.
Horrian, S., Bahrami, S. (2020). 'Estimation of the Payload mass in DELTA robot by using the motor torque', Electronic and Cyber Defense, 16(2), pp. 27-38.
Horrian, S., Bahrami, S. Estimation of the Payload mass in DELTA robot by using the motor torque. Electronic and Cyber Defense, 2020; 16(2): 27-38.

Estimation of the Payload mass in DELTA robot by using the motor torque

مکانیک هوافضا
Article 3, Volume 16, Issue 2 - Serial Number 60, June 2020, Pages 27-38    PDF (507.65 K)
Document Type: Dynamics, Vibrations, and Control
Authors
Siavash Horrian; Saeed Bahrami*
Department of Mechatronics Karaj Islamic Azad University
Receive Date: 11 May 2019,  Revise Date: 29 June 2019,  Accept Date: 14 June 2020 
Abstract
Controlling product weight before packing is one of the main quality control features of many manufacturing processes. Although this issue can be done by the usual methods such as measuring scale, it can reduce the speed of the product line which is equipped with fast machines such as the delta robot. This paper presents an online method for the identification of the payload mass in the delta robot during the packing process. For this purpose, the governing dynamic equations of the robot has been written and after validating them by the ADAMS software, they are rewritten in linear regression form for the identification process. Then, the recursive least square method is used for the online parameters identification. Moreover, since the assumption of constant friction coefficients during the robot operation can reduce the estimation accuracy, these parameters are also estimated in each step with the payload mass. The proposed method is implemented in MATLAB environment and the simulation results illustrates the sufficient accuracy of the suggested method even in the presence of the measurement noise. Furthermore, the simulations indicate that the method have high convergence rate and the payload mass can be estimated with a small number of data and in a short time.
Keywords
Delta robot; Friction identification; Payload identification; Recursive Least Square
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