Iterative learning control algorithms based on complex stochastic distribution systems

  • Yang Yi*
  • , Changyin Sun
  • , Lei Guo
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, a new generalized iterative learning algorithm is first proposed based on complex non-Gaussion stochastic control systems. Following designed neural networks are used to approximate the output PDF of the stochastic system in the repetitive processes or the batch processes, the tracking control to PDF is transformed into a parameter adaptive tuning problem in NN basis function. Under this framework, we study a new model free iterative learning control problem and propose a convex optimization algorithm based on a set of designed LMIs and L1 performance index. Such an algorithm has the advantage of the improvement of the closed-loop output PDF tracking performance and robustness. Simulation results are given to demonstrate the effectiveness of the proposed approach.

Original languageEnglish
Title of host publicationProceedings of the 30th Chinese Control Conference, CCC 2011
Pages1367-1371
Number of pages5
StatePublished - 2011
Event30th Chinese Control Conference, CCC 2011 - Yantai, China
Duration: 22 Jul 201124 Jul 2011

Publication series

NameProceedings of the 30th Chinese Control Conference, CCC 2011

Conference

Conference30th Chinese Control Conference, CCC 2011
Country/TerritoryChina
CityYantai
Period22/07/1124/07/11

Keywords

  • Iterative Learning Control
  • L Optimization Index
  • Stochastic Distribution Control

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