Abstract
A novel Kalman filter-based adaptive observer for the sampled-data nonlinear time-varying system is proposed in this paper. With the high gain property of Kalman filter, it is applicable to a large variation of unknown parameters, which can be estimated optimally. Then a method of actuator fault detection is proposed. With the estimated faults, one can use the proposed input compensation method to solve actuator faults. Additionally, the optimal linearization technique is used to obtain the locally optimal linear model for a nonlinear system at each sampled state, so that the actuator fault detection and performance recovery of a sampled-data nonlinear time-varying system is accomplished. In this paper, we also introduce a prediction-based digital redesign method to develop the corresponding sampled-data controller.
| Original language | English |
|---|---|
| Pages (from-to) | 375-398 |
| Number of pages | 24 |
| Journal | International Journal of General Systems |
| Volume | 36 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 2007 Aug 1 |
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All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Theoretical Computer Science
- Information Systems
- Modelling and Simulation
- Computer Science Applications
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Actuator fault detection and performance recovery with Kalman filter-based adaptive observer. / Tsai, Jason Sheng-Hon; Lin, Ming Hong; Zheng, Chen Hong; Guo, Shu-Mei; Shieh, Leang San.
In: International Journal of General Systems, Vol. 36, No. 4, 01.08.2007, p. 375-398.Research output: Contribution to journal › Article
TY - JOUR
T1 - Actuator fault detection and performance recovery with Kalman filter-based adaptive observer
AU - Tsai, Jason Sheng-Hon
AU - Lin, Ming Hong
AU - Zheng, Chen Hong
AU - Guo, Shu-Mei
AU - Shieh, Leang San
PY - 2007/8/1
Y1 - 2007/8/1
N2 - A novel Kalman filter-based adaptive observer for the sampled-data nonlinear time-varying system is proposed in this paper. With the high gain property of Kalman filter, it is applicable to a large variation of unknown parameters, which can be estimated optimally. Then a method of actuator fault detection is proposed. With the estimated faults, one can use the proposed input compensation method to solve actuator faults. Additionally, the optimal linearization technique is used to obtain the locally optimal linear model for a nonlinear system at each sampled state, so that the actuator fault detection and performance recovery of a sampled-data nonlinear time-varying system is accomplished. In this paper, we also introduce a prediction-based digital redesign method to develop the corresponding sampled-data controller.
AB - A novel Kalman filter-based adaptive observer for the sampled-data nonlinear time-varying system is proposed in this paper. With the high gain property of Kalman filter, it is applicable to a large variation of unknown parameters, which can be estimated optimally. Then a method of actuator fault detection is proposed. With the estimated faults, one can use the proposed input compensation method to solve actuator faults. Additionally, the optimal linearization technique is used to obtain the locally optimal linear model for a nonlinear system at each sampled state, so that the actuator fault detection and performance recovery of a sampled-data nonlinear time-varying system is accomplished. In this paper, we also introduce a prediction-based digital redesign method to develop the corresponding sampled-data controller.
UR - http://www.scopus.com/inward/record.url?scp=34047197015&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=34047197015&partnerID=8YFLogxK
U2 - 10.1080/03081070600928963
DO - 10.1080/03081070600928963
M3 - Article
VL - 36
SP - 375
EP - 398
JO - International Journal of General Systems
JF - International Journal of General Systems
SN - 0308-1079
IS - 4
ER -