An Active Fault-Tolerant PWM Tracker for Unknown Nonlinear Stochastic Hybrid Systems: NARMAX Model and OKID-Based State-Space Self-Tuning Control
| Autor: | Leang-San Shieh, Shu-Mei Guo, You Lin, Jason Sheng Hong Tsai, Chia-Wei Chen, Chu-Tong Wang |
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| Jazyk: | angličtina |
| Rok vydání: | 2010 |
| Předmět: |
Engineering
Observer (quantum physics) Article Subject business.industry Covariance matrix Self-tuning Kalman filter Fault detection and isolation lcsh:QA75.5-76.95 Computer Science Applications Nonlinear system Control theory lcsh:TA1-2040 Modeling and Simulation Hybrid system Autoregressive–moving-average model lcsh:Electronic computers. Computer science Electrical and Electronic Engineering business lcsh:Engineering (General). Civil engineering (General) |
| Zdroj: | Journal of Control Science and Engineering, Vol 2010 (2010) |
| ISSN: | 1687-5257 1687-5249 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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