An active fault-tolerant PWM tracker for unknown nonlinear stochastic hybrid systems: NARMAX model and OKID-based state-space self-tuning control

Jason Sheng-Hon Tsai, Chu Tong Wang, Chia Wei Chen, You Lin, Shu-Mei Guo, Leang San Shieh

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

An active fault-tolerant pulse-width-modulated tracker using the nonlinear autoregressive moving average with exogenous inputs model-based state-space self-tuning control is proposed for continuous-time multivariable nonlinear stochastic systems with unknown system parameters, plant noises, measurement noises, and inaccessible system states. Through observer/Kalman filter identification method, a good initial guess of the unknown parameters of the chosen model is obtained so as to reduce the identification process time and enhance the system performances. Besides, by modifying the conventional self-tuning control, a fault-tolerant control scheme is also developed. For the detection of fault occurrence, a quantitative criterion is exploited by comparing the innovation process errors estimated by the Kalman filter estimation algorithm. In addition, the weighting matrix resetting technique is presented by adjusting and resetting the covariance matrix of parameter estimates to improve the parameter estimation for faulty system recovery. The technique can effectively cope with partially abrupt and/or gradual system faults and/or input failures with fault detection.

Original languageEnglish
Article number217515
JournalJournal of Control Science and Engineering
Volume2010
DOIs
Publication statusPublished - 2010 Aug 11

All Science Journal Classification (ASJC) codes

  • Modelling and Simulation
  • Computer Science Applications
  • Electrical and Electronic Engineering

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