An improvement on the transient response of tracking for the sampled-data system based on an improved PD-type iterative learning control

Fu Ming Chen, Jason Sheng Hong Tsai, Ying Ting Liao, Shu Mei Guo, Ming Chung Ho, Fu Zen Shaw, Leang San Shieh

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)

Abstract

An improvement on the transient response of tracking for the sampled-data system based on an improved PD-type iterative learning control (ILC) is proposed in this paper. The developed analog ILC method and the high-gain property tracker design methodology are first combined to significantly reduce learning epochs and overcome the initial condition shift problem and discontinuous reference input in the traditional ILC. Besides, the proposed ILC improves the transient response and decreases the rate of weighting matrices Q to R under the traditional linear quadratic tracker design. First, the off-line observer/Kalman filter identification (OKID) is used to determine the appropriate (low-) order system parameters and state estimator for the physical system with unknown system equation, so that the model-based PD-type ILC can be implemented for practical applications. Then, to improve the transient response and decrease the control effort, the proportional difference type (PD-type) ILC algorithm is combined with the high-gain property linear quadratic tracker (LQT) design to construct the high performance tracker for the model-based sampled-data systems. Furthermore, the discrete-time version high performance tracker design for the unknown stochastic sampled-data system via the iterative learning control method is proposed in this paper based on the Euler method and the digital redesign approach. Finally, some examples are given for illustrating the effectiveness of the proposed method.

Original languageEnglish
Pages (from-to)1130-1150
Number of pages21
JournalJournal of the Franklin Institute
Volume351
Issue number2
DOIs
Publication statusPublished - 2014 Feb

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications
  • Applied Mathematics

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