An active low-order fault-tolerant state space self-tuner for the unknown sample-data linear regular system with an input-output direct feedthrough term

Jyh Haw Wang, Jason Sheng-Hon Tsai, Yong Cheng Chen, Shu-Mei Guo, Leang San Shieh

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

A novel active low-order fault-tolerant state space self-tuner for the unknown linear regular system with an input-output direct feedthrough term matrix using observer/Kalman filter identification (OKID) and modified autoregressive moving average with exogenous input (ARMAX) model-based system identification is proposed in this paper. Through OKID, the order determination and a good initial guess of the modified ARMAX model is obtained to improve the performance of the identification process. With the modified adjustable ARMAX-based system identification, a corresponding adaptive digital control scheme is recommended. Besides, by modifying the conventional self-tuning control, a fault tolerant control scheme is also developed for the system. With the detection of fault occurrence, a quantitative criterion is improved by comparing the innovation process errors estimated by the Kalman filter estimation algorithm. Therefore, a resetting technique of the weighting matrix is amended by adjusting and resetting the covariance matrices of parameter estimation obtained by the Kalman filter estimation algorithm.

Original languageEnglish
Pages (from-to)4813-4855
Number of pages43
JournalApplied Mathematical Sciences
Volume6
Issue number97-100
Publication statusPublished - 2012 Oct 16

All Science Journal Classification (ASJC) codes

  • Applied Mathematics

Fingerprint Dive into the research topics of 'An active low-order fault-tolerant state space self-tuner for the unknown sample-data linear regular system with an input-output direct feedthrough term'. Together they form a unique fingerprint.

  • Cite this