Application of parsimonious learning feedforward control to mechatronic systems

T.J.A. de Vries, W.J.R. Velthuis, L.J. Idema

    Research output: Contribution to journalArticleAcademic

    9 Citations (Scopus)


    For motion control, learning feedforward controllers (LFFCs) should be applied when accurate process modelling is difficult. When controlling such processes with LFFCs in the form of multidimensional B-spline networks, large network sizes and a poor generalising ability may result, known as the curse of dimensionality. Therefore, a parsimonious (reduced dimensionality) LFFC is required. Empirical modelling methods are not suited to obtain parsimonious networks for highly nonlinear processes because large data sets are needed. Alternatively, (qualitative) process knowledge can be used to construct parsimonious LFF controllers. In the research reported, a parsimonious LFFC was applied to a linear motor motion system. The experiments showed fast learning, good network parsimony, and small tracking errors for a range of motions.
    Original languageEnglish
    Pages (from-to)318-322
    JournalIEE proceedings - Control theory and applications
    Issue number4
    Publication statusPublished - 2001

    Fingerprint Dive into the research topics of 'Application of parsimonious learning feedforward control to mechatronic systems'. Together they form a unique fingerprint.

    Cite this