Robust Backlash Estimation for Industrial Drive-Train Systems—Theory and Validation

Dimitrios Papageorgiou*, Mogens Blanke, Hans Henrik Niemann, Jan H. Richter

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Backlash compensation is used in modern machinetool controls to ensure high-accuracy positioning. When wear of a machine causes deadzone width to increase, high-accuracy control may be maintained if the deadzone is accurately estimated. Deadzone estimation is also an important parameter to indicate the level of wear in a machine transmission, and tracking its development is essential for condition-based maintenance. This paper addresses the backlash estimation problem using sliding-mode and adaptive estimation principles and shows that prognosis of the development of wear is possible in both theory and practice. This paper provides the proof of asymptotic convergence of the suggested estimator, and it shows how position offset between motor and load is efficiently utilized in the design of a very efficient estimator. The algorithm is experimentally tested on a drive-train system with the state-of-the-art Siemens equipment. The experiments validate the theory and show that expected performance and robustness to parameter uncertainties are both achieved.
Original languageEnglish
JournalIEEE Transactions on Control Systems Technology
Volume27
Issue number5
Pages (from-to)1847-1861
ISSN1063-6536
DOIs
Publication statusPublished - 2019

Keywords

  • Adaptive deadzone estimation
  • Backlash estimation
  • Experimental validation
  • Machine tools
  • Mechanical drive train
  • Nonlinear parameterization
  • Parameter estimation
  • Robustness analysis
  • Sliding-mode observer (SMO)

Cite this

@article{1782010fbf454b9896fbfbf7fdfb7db2,
title = "Robust Backlash Estimation for Industrial Drive-Train Systems—Theory and Validation",
abstract = "Backlash compensation is used in modern machinetool controls to ensure high-accuracy positioning. When wear of a machine causes deadzone width to increase, high-accuracy control may be maintained if the deadzone is accurately estimated. Deadzone estimation is also an important parameter to indicate the level of wear in a machine transmission, and tracking its development is essential for condition-based maintenance. This paper addresses the backlash estimation problem using sliding-mode and adaptive estimation principles and shows that prognosis of the development of wear is possible in both theory and practice. This paper provides the proof of asymptotic convergence of the suggested estimator, and it shows how position offset between motor and load is efficiently utilized in the design of a very efficient estimator. The algorithm is experimentally tested on a drive-train system with the state-of-the-art Siemens equipment. The experiments validate the theory and show that expected performance and robustness to parameter uncertainties are both achieved.",
keywords = "Adaptive deadzone estimation, Backlash estimation, Experimental validation, Machine tools, Mechanical drive train, Nonlinear parameterization, Parameter estimation, Robustness analysis, Sliding-mode observer (SMO)",
author = "Dimitrios Papageorgiou and Mogens Blanke and Niemann, {Hans Henrik} and Richter, {Jan H.}",
year = "2019",
doi = "10.1109/TCST.2018.2837642",
language = "English",
volume = "27",
pages = "1847--1861",
journal = "I E E E Transactions on Control Systems Technology",
issn = "1063-6536",
publisher = "Institute of Electrical and Electronics Engineers",
number = "5",

}

Robust Backlash Estimation for Industrial Drive-Train Systems—Theory and Validation. / Papageorgiou, Dimitrios; Blanke, Mogens; Niemann, Hans Henrik; Richter, Jan H.

In: IEEE Transactions on Control Systems Technology, Vol. 27, No. 5, 2019, p. 1847-1861.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - Robust Backlash Estimation for Industrial Drive-Train Systems—Theory and Validation

AU - Papageorgiou, Dimitrios

AU - Blanke, Mogens

AU - Niemann, Hans Henrik

AU - Richter, Jan H.

PY - 2019

Y1 - 2019

N2 - Backlash compensation is used in modern machinetool controls to ensure high-accuracy positioning. When wear of a machine causes deadzone width to increase, high-accuracy control may be maintained if the deadzone is accurately estimated. Deadzone estimation is also an important parameter to indicate the level of wear in a machine transmission, and tracking its development is essential for condition-based maintenance. This paper addresses the backlash estimation problem using sliding-mode and adaptive estimation principles and shows that prognosis of the development of wear is possible in both theory and practice. This paper provides the proof of asymptotic convergence of the suggested estimator, and it shows how position offset between motor and load is efficiently utilized in the design of a very efficient estimator. The algorithm is experimentally tested on a drive-train system with the state-of-the-art Siemens equipment. The experiments validate the theory and show that expected performance and robustness to parameter uncertainties are both achieved.

AB - Backlash compensation is used in modern machinetool controls to ensure high-accuracy positioning. When wear of a machine causes deadzone width to increase, high-accuracy control may be maintained if the deadzone is accurately estimated. Deadzone estimation is also an important parameter to indicate the level of wear in a machine transmission, and tracking its development is essential for condition-based maintenance. This paper addresses the backlash estimation problem using sliding-mode and adaptive estimation principles and shows that prognosis of the development of wear is possible in both theory and practice. This paper provides the proof of asymptotic convergence of the suggested estimator, and it shows how position offset between motor and load is efficiently utilized in the design of a very efficient estimator. The algorithm is experimentally tested on a drive-train system with the state-of-the-art Siemens equipment. The experiments validate the theory and show that expected performance and robustness to parameter uncertainties are both achieved.

KW - Adaptive deadzone estimation

KW - Backlash estimation

KW - Experimental validation

KW - Machine tools

KW - Mechanical drive train

KW - Nonlinear parameterization

KW - Parameter estimation

KW - Robustness analysis

KW - Sliding-mode observer (SMO)

U2 - 10.1109/TCST.2018.2837642

DO - 10.1109/TCST.2018.2837642

M3 - Journal article

VL - 27

SP - 1847

EP - 1861

JO - I E E E Transactions on Control Systems Technology

JF - I E E E Transactions on Control Systems Technology

SN - 1063-6536

IS - 5

ER -