An engineering method for the power flow assessment in servo-actuated automated machinery

In this paper, an engineering method for the power flow assessment of a position-controlled servo-mechanism is outlined. The considered system is composed of a permanent magnet synchronous motor coupled to a standard power converter, and directly connected to a slider crank mechanism. After the accurate description of a consistent power flow model, a sequential identification technique is discussed, which allows to determine the dynamic parameters of linkage, electric motor and electronic driver by means of non-invasive experimental measures. The proposed model allows to accurately predict the major sources of power loss within the system. HighlightsDescription of a Power Flow (PF) model within a servo-actuated mechanism.PF employing a Linear-in-Parameters (LP) formulation.Description of a sequential PF identification method.Experimental validation on an industrial case study.

[1]  John G. Webster,et al.  Electrical Measurement, Signal Processing, and Displays , 2003 .

[2]  Tomonobu Senjyu,et al.  Influence of all losses on permanent magnet synchronous motor drives , 2000, 2000 26th Annual Conference of the IEEE Industrial Electronics Society. IECON 2000. 2000 IEEE International Conference on Industrial Electronics, Control and Instrumentation. 21st Century Technologies.

[3]  M. Gautier,et al.  Exciting Trajectories for the Identification of Base Inertial Parameters of Robots , 1991, [1991] Proceedings of the 30th IEEE Conference on Decision and Control.

[4]  Tobias Ortmaier,et al.  Enhanced approach for energy-efficient trajectory generation of industrial robots , 2012, 2012 IEEE International Conference on Automation Science and Engineering (CASE).

[5]  Wisama Khalil,et al.  Modeling, Identification and Control of Robots , 2003 .

[6]  Bimal K. Bose,et al.  Modern Power Electronics and AC Drives , 2001 .

[7]  Marcello Pellicciari,et al.  On Designing Optimal Trajectories for Servo-Actuated Mechanisms: Detailed Virtual Prototyping and Experimental Evaluation , 2015, IEEE/ASME Transactions on Mechatronics.

[8]  Brian Armstrong.  On finding 'exciting' trajectories for identification experiments involving systems with non-linear dynamics , 1987, Proceedings. 1987 IEEE International Conference on Robotics and Automation.

[9]  G. Oriolo,et al.  Robotics: Modelling, Planning and Control , 2008 .

[10]  Marcello Pellicciari,et al.  Energy Efficient Use of Multirobot Production Lines in the Automotive Industry: Detailed System Modeling and Optimization , 2014, IEEE Transactions on Automation Science and Engineering.

[11]  Sébastien Briot,et al.  New method for global identification of the joint drive gains of robots using a known payload mass , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[12]  Carlos Canudas de Wit,et al.  Friction Models and Friction Compensation , 1998, Eur. J. Control.

[13]  Rong-Fong Fung,et al.  Comparison between mathematical modeling and experimental identification of a spatial slider–crank mechanism , 2010 .

[14]  Sébastien Briot,et al.  New method for global identification of the joint drive gains of robots using a known inertial payload , 2011, IEEE Conference on Decision and Control and European Control Conference.

[15]  Jun Wu,et al.  Review: An overview of dynamic parameter identification of robots , 2010 .

[16]  M. Gautier Robet.  Decoupled identification of electrical and mechanical parameters of synchronous motor-driven chain with an efficient CLOE method , 2013, 2013 IEEE 8th Conference on Industrial Electronics and Applications (ICIEA).

[17]  Jens Kotlarski,et al.  Experimental validation of advanced minimum energy robot trajectory optimization , 2013, 2013 16th International Conference on Advanced Robotics (ICAR).

[18]  P. Poignet,et al.  Extended Kalman filtering and weighted least squares dynamic identification of robot , 2000 .

[19]  Christopher G. Atkeson,et al.  Estimation of Inertial Parameters of Manipulator Loads and Links , 1986 .

[20]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[21]  Marcello Pellicciari,et al.  A method for reducing the energy consumption of pick-and-place industrial robots , 2013 .

[22]  Philippe Poignet,et al.  Comparison of weighted least squares and extended Kalman filtering methods for dynamic identification of robots , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[23]  Maxime Gautier,et al.  Dynamic identification of robots with a dry friction model depending on load and velocity , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[24]  Paul Mativenga,et al.  Modelling of direct energy requirements in mechanical machining processes , 2013 .

[25]  Giovanni Berselli,et al.  Dynamic Identification of Industrial Robots from Low-Sampled Data , 2013 .

[26]  J. De Schutter,et al.  Dynamic Model Identification for Industrial Robots , 2007, IEEE Control Systems.

[27]  Rong-Fong Fung,et al.  Dynamic modeling and identification of a slider-crank mechanism , 2006 .