An Active Fault-Tolerant PWM Tracker for Unknown Nonlinear Stochastic Hybrid Systems: NARMAX Model and OKID-Based State-Space Self-Tuning Control
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 01, 2010
- Source ID
- 10.1155/2010/217515
Entities
People
- Chia-wei Chen
- Chu-tong Wang
- Jason S. H. Tsai
- Leang-san Shieh
- Shu-mei Guo
- You Lin
Organizations
- Army Research Office
- Department of Computer Science, University of Oxford
- Tàiwan Steel University of Science and Technology
- University of Houston