Decentralized iterative learning control to large-scale industrial processes for nonrepetitive trajectory tracking

Cited 48 time in webofscience Cited 0 time in scopus
  • Hit : 300
  • Download : 0
In the procedure of steady-state hierarchical optimization with feedback for a large-scale industrial process, it is usual that a sequence of step set-point changes is carried out and used by the decision-making units while searching the eventual optimum. In this case, the real process experiences a form of disturbances around its operating set-point. In order to improve the dynamic performance of transient responses for such a large-scale system driven by the set-point changes, an open-loop proportional integral derivative-type iterative learning control (ILC) strategy is explored in this paper by considering the different magnitudes of the controller's step set-point change sequence. Utilizing the Hausdorff-Young inequality of convolution integral, the convergence of the algorithm is derived in the sense of Lebesgue-P norm. Furthermore, the extended higher order ILC rule is developed, and the convergence is analyzed. Simulation results illustrate that the proposed ILC strategies can remarkably improve the dynamic performance such as decreasing the overshoot, accelerating the transient response, shortening the settling time, etc.
Publisher
IEEE-Inst Electrical Electronics Engineers Inc
Issue Date
2008-01
Language
English
Article Type
Article
Keywords

FUZZY-LOGIC CONTROLLER; NONLINEAR-SYSTEMS; QUADRATIC CRITERION; TIME-SYSTEMS; SUP-NORM; CONVERGENCE; ALGORITHM

Citation

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS, v.38, pp.238 - 252

ISSN
1083-4427
URI
http://hdl.handle.net/10203/93267
Appears in Collection
EE-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 48 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0