IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-540-27170-3_23.html
   My bibliography  Save this book chapter

Design of a Noncausal FIR Model Inverse as a Compensator in Repetitive Control

In: Modeling, Simulation and Optimization of Complex Processes

Author

Listed:
  • Richard W. Longman

    (Columbia University)

  • Benjamas Panomruttanarug

    (Columbia University)

Abstract

Summary A new method of designing compensators for repetitive controllers is presented. The ideal compensator is a filter that is the inverse of the plant, but this is usually unstable, and therefore cannot be used in practice. The approach used here works on a restricted class of transfer functions, and bypasses this difficulty by making a noncausal FIR model of the plant inverse. This model has poles only at the origin, and is therefore stable. Methods are presented to adjust the three parameters of the design for stability and good learning speed, i.e. the repetitive control gain, and the number of causal, and the number of noncausal gains chosen to compose the finite impulse response model. A third order system is studied, which models the closed loop behavior of one link of a commercial robot. One can produce a stable design with a number of gains ranging from 11 to 15 (this gives the number of real time computations for control update), and these numbers are not particularly sensitive to sample rate. Using 18, 20, or 30 gains can produce a quite reasonable plant inverse model that gives fast learning in repetitions at all frequencies.

Suggested Citation

  • Richard W. Longman & Benjamas Panomruttanarug, 2005. "Design of a Noncausal FIR Model Inverse as a Compensator in Repetitive Control," Springer Books, in: Hans Georg Bock & Hoang Xuan Phu & Ekaterina Kostina & Rolf Rannacher (ed.), Modeling, Simulation and Optimization of Complex Processes, pages 297-306, Springer.
    • Handle: RePEc:spr:sprchp:978-3-540-27170-3_23
    DOI: 10.1007/3-540-27170-8_23
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:sprchp:978-3-540-27170-3_23. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.