Model-based iterative learning control with a quadratic criterion for time-varying linear systems

Cited 313 time in webofscience Cited 396 time in scopus
  • Hit : 399
  • Download : 0
In this paper, iterative learning control (ILC) based on a quadratic performance criterion is revisited and generalized for time-varying linear constrained systems with deterministic, stochastic disturbances and noises. The main intended area of application for this generalized method is chemical process control, where excessive input movements are undesirable and many process variables are subject to hard constraints. It is shown that, within the framework of the quadratic-criterion-based ILC (Q-ILC), various practical issues such as constraints, disturbances, measurement noises, and model errors can be considered in a rigorous and systematic manner. Algorithms for the deterministic case, the stochastic case, and the case with bounded parameter uncertainties are developed and relevant properties such as the asymptotic convergence are established under some mild assumptions. Numerical examples are provided to demonstrate the performance of the proposed algorithms. (C) 2000 Elsevier Science Ltd. All rights reserved.
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Issue Date
2000-05
Language
English
Article Type
Article; Proceedings Paper
Citation

AUTOMATICA, v.36, no.5, pp.641 - 657

ISSN
0005-1098
DOI
10.1016/S0005-1098(99)00194-6
URI
http://hdl.handle.net/10203/71324
Appears in Collection
CBE-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 313 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0