학술논문

Robust Backlash Estimation for Industrial Drive-Train Systems—Theory and Validation
Document Type
Periodical
Source
IEEE Transactions on Control Systems Technology IEEE Trans. Contr. Syst. Technol. Control Systems Technology, IEEE Transactions on. 27(5):1847-1861 Sep, 2019
Subject
Signal Processing and Analysis
Communication, Networking and Broadcast Technologies
Computing and Processing
Robotics and Control Systems
Estimation
Torque
Adaptation models
Load modeling
Shafts
Permanent magnet motors
Synchronous motors
Adaptive deadzone estimation
backlash estimation
experimental validation
machine tools
mechanical drive train
nonlinear parameterization
parameter estimation
robustness analysis
sliding-mode observer (SMO)
Language
ISSN
1063-6536
1558-0865
2374-0159
Abstract
Backlash compensation is used in modern machine-tool 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.