Development of command-based iterative learning control algorithm with consideration of friction, disturbance, and noise effects

In this brief, a command-based iterative learning control (ILC) architecture is proposed to compensate for friction effect and to reduce tracking error caused by servo lag. In contrast to a feedback-feedforward control structure, the proposed methodology utilizes the learning algorithm that updates...

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Bibliographic Details
Published in:IEEE transactions on control systems technology 2006-05, Vol.14 (3), p.511-518
Main Authors: TSAI, Meng-Shiun, LIN, Ming-Tzong, YAU, Hong-Tzong
Format: Article
Language:English
Subjects:
Adaptative systems
Algorithms
Applied sciences
Command-based iterative learning control (ILC)
Commands
Computer science
control theory
systems
Control system synthesis
Control theory. Systems
Equations
Error correction
Exact sciences and technology
Filters
Friction
friction compensation
Iterative algorithms
Learning
Machining
Mathematical analysis
Mechanical engineering. Machine design
Noise
Noise shaping
noise/disturbance accumulation
Performance analysis
Servomechanisms
Shape
Tracking
Tracking errors
zero-phase filtering
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Description
Summary:In this brief, a command-based iterative learning control (ILC) architecture is proposed to compensate for friction effect and to reduce tracking error caused by servo lag. In contrast to a feedback-feedforward control structure, the proposed methodology utilizes the learning algorithm that updates the input commands based on the tracking errors from the previous machining process. The effects of noise accumulations from each learning process of the ILC are analyzed by formulating the equivalent error dynamic and updated command equations, and the P-type ILC with a zero-phase filter is applied to alleviate noise and disturbance effects. It is shown that, for tracking a circle, the quadrant protrusions caused by friction can be reduced substantially by the updated command containing a concave shape located at the crossing of the zero velocity. Finally, analytical simulation and experimental results demonstrate that the command-based ILC algorithm can enhance the tracking performance significantly.
ISSN:1063-6536
1558-0865
DOI:10.1109/TCST.2005.860521