TY - JOUR
T1 - Actuator fault detection and performance recovery with Kalman filter-based adaptive observer
AU - Tsai, Jason Sheng Hong
AU - Lin, Ming Hong
AU - Zheng, Chen Hong
AU - Guo, Shu Mei
AU - Shieh, Leang San
N1 - Funding Information:
This work was supported by the National Science Council of Republic of China under contracts NSC-94-2213-E-006-067, NSC-94-2213-E-006-068, the Texas DOT under contract No. 466PVIA003 and NASA-JSC under grant NNJ04HF32G.
PY - 2007/8
Y1 - 2007/8
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.
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U2 - 10.1080/03081070600928963
DO - 10.1080/03081070600928963
M3 - Article
AN - SCOPUS:34047197015
VL - 36
SP - 375
EP - 398
JO - International Journal of General Systems
JF - International Journal of General Systems
SN - 0308-1079
IS - 4
ER -