Learning Control Without Prior Models: Multi-Variable Model-Free IIC, with application to a Wide-Format Printer. Issue 15 (2019)

• Record Type: Journal Article
• Authors: Rozario, Robin de; Oomen, Tom
• Is Part Of: IFAC-PapersOnLine. Volume 52:Issue 15(2019)
• Page Start: 91
• Page End: 96
• Publication Date: 2019
• Subjects: Frequency response methods -- Linear multivariable systems -- System identification -- Convergence analysis -- Nonlinear analysis; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2019.11.656
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: Learning control enables performance improvement of mechatronic systems that operate in a repetitive manner. Achieving desirable learning behavior typically requires prior knowledge in the form of a model. The prior modeling requirements can be significantly reduced by using past operational data to estimate this model during the learning process. The aim of this paper is to develop such a data-driven learning control method for multi-variable systems, which requires that directionality aspects are properly addressed. This is achieved by using multiple past experiments to estimate a frequency response function of the inverse dynamics while ensuring smooth convergence by using smoothed pseudo inversion. The developed method is successfully applied to an industrial wide-format printer, resulting in high performance.