Proceedings ArticleDOI
On inferential iterative learning control : with example on a printing system
JJ Joost Bolder,Tae Tom Oomen,M Maarten Steinbuch +2 more
- pp 1827-1832
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TLDR
Recent developments in inferential control are utilized to arrive at control structures suited for inferential ILC, and proposed frameworks extend earlier results and encompass various controller structures.Abstract:
Since performance variables cannot be measured directly, Iterative Learning Control (ILC) is usually applied to measured variables. In this paper, it is shown that this can deteriorate performance. New batch-wise sensors that measure the performance variables directly are well-suited for use in ILC and can potentially improve performance. In this paper, recent developments in inferential control are utilized to arrive at control structures suited for inferential ILC. The proposed frameworks extend earlier results and encompass various controller structures. The results are supported with a simulation example.read more
Citations
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Journal ArticleDOI
Using iterative learning control with basis functions to compensate medium deformation in a wide-format inkjet printer
TL;DR: In this article, an iterative learning control (ILC) algorithm is proposed to compensate the paper deformation by actively changing the longitudinal paper position during a lateral pass of the printheads.
Journal ArticleDOI
Inferential Iterative Learning Control:A 2D-system approach
JJ Joost Bolder,Tae Tom Oomen +1 more
TL;DR: The aim of this paper is to show that the pre-existing ILC controllers may not be directly implementable in this setting, and to develop a new approach that enables the use of different variables for feedback and batch-to-batch control.
Journal ArticleDOI
Inferential Motion Control: Identification and Robust Control Framework for Positioning an Unmeasurable Point of Interest
TL;DR: Experimental results on a prototype motion system reveal that ignoring internal deformations using traditional motion control design approaches can lead to disastrous performance at the point of interest, and it is shown that the proposed inferential motion control framework leads to highperformance at the unmeasurable point ofinterest.
Journal ArticleDOI
Periodic Signal Tracking for Lightly Damped Systems
TL;DR: In this paper, a general approach for the zero-phase tracking of periodic inputs is presented followed by an illustration of single harmonic tracking of underdamped second-order systems with relative degree two.
Flexibility and robustness in iterative learning control : with applications to industrial printers
TL;DR: The final author version and the galley proof are versions of the publication after peer review and the final published version features the final layout of the paper including the volume, issue and page numbers.
References
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Book
Multivariable Feedback Control: Analysis and Design
TL;DR: This book presents a rigorous, yet easily readable, introduction to the analysis and design of robust multivariable control systems and provides the reader with insights into the opportunities and limitations of feedback control.
Journal ArticleDOI
A survey of iterative learning control
TL;DR: Though beginning its third decade of active research, the field of ILC shows no sign of slowing down and includes many results and learning algorithms beyond the scope of this survey.
Book
Iterative Learning Control for Deterministic Systems
TL;DR: The material presented in this book addresses the analysis and design of learning control systems using a system-theoretic approach, and the application of artificial neural networks to the learning control problem.
Journal ArticleDOI
Iterative learning control using optimal feedback and feedforward actions
TL;DR: An algorithm for iterative learning control is developed on the basis of an optimization principle which has been used previously to derive gradient-type algorithms and has numerous benefits which include realization in terms of Riccati feedback and feedforward components.
Journal ArticleDOI
Brief On the design of ILC algorithms using optimization
Svante Gunnarsson,Mikael Norrlöf +1 more
TL;DR: The focus is on the frequency domain properties of the algorithm, and how it is able to handle non-minimum phase systems.