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

  • Authors:

  • Chu-Tong Wang;Jason S. H. Tsai;Chia-Wei Chen;You Lin;Shu-Mei Guo;Leang-San Shieh

  • Affiliations:

  • Department of Electrical Engineering, National Cheng-Kung University, Tainan, Taiwan;Department of Electrical Engineering, National Cheng-Kung University, Tainan, Taiwan;Department of Mechanical and Automation Engineering, Kao Yuan University, Kaohsiung, Taiwan;Department of Electrical Engineering, National Cheng-Kung University, Tainan, Taiwan;Department of Computer Science and Information Engineering, National Cheng-Kung University, Tainan, Taiwan;Department of Electrical Computer Engineering, University of Houston, Houston, TX

  • Venue:
  • Journal of Control Science and Engineering
  • Year:
  • 2010

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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.