TY - JOUR
T1 - Adaptive Neural Network-Based Filter Design for Nonlinear Systems with Multiple Constraints
AU - Shen, Qikun
AU - Shi, Peng
AU - Agarwal, Ramesh K.
AU - Shi, Yan
N1 - Funding Information:
Manuscript received December 11, 2019; revised April 29, 2020; accepted July 9, 2020. Date of publication July 28, 2020; date of current version July 7, 2021. This work was supported in part by the National Natural Science Foundation of China under Grant 61873229 and in part by the Key Research Project of Department of Education, Chongqing, under Grant KJZD-M201900801. (Corresponding author: Qikun Shen.) Qikun Shen is with the College of Information Engineering, Yangzhou University, Yangzhou 225127, China (e-mail: [email protected]).
Publisher Copyright:
© 2012 IEEE.
PY - 2021/7
Y1 - 2021/7
N2 - Filter design for nonlinear systems, especially time delayed nonlinear systems, has always been an important and challenging problem. This brief investigates the filter design problem of nonlinear systems with multiple constraints: time delay, actuator, and sensor faults, and a new adaptive neural network-based filter design method is proposed. Comparing with the existing works where there is a shortcoming that the designed filters contain unknown time delay(s), the design method proposed in this brief overcomes the shortcoming and only the estimation of the unknown time delay exists in the filter. Furthermore, not only the system states can be estimated, but also the unknown time delay with actuator and sensor faults can be estimated in this brief. Finally, simulation results are given to show the effectiveness of the proposed new design method.
AB - Filter design for nonlinear systems, especially time delayed nonlinear systems, has always been an important and challenging problem. This brief investigates the filter design problem of nonlinear systems with multiple constraints: time delay, actuator, and sensor faults, and a new adaptive neural network-based filter design method is proposed. Comparing with the existing works where there is a shortcoming that the designed filters contain unknown time delay(s), the design method proposed in this brief overcomes the shortcoming and only the estimation of the unknown time delay exists in the filter. Furthermore, not only the system states can be estimated, but also the unknown time delay with actuator and sensor faults can be estimated in this brief. Finally, simulation results are given to show the effectiveness of the proposed new design method.
KW - Adaptive filter design
KW - fault estimation
KW - time delay
UR - http://www.scopus.com/inward/record.url?scp=85097741494&partnerID=8YFLogxK
U2 - 10.1109/TNNLS.2020.3009391
DO - 10.1109/TNNLS.2020.3009391
M3 - Article
C2 - 32721902
AN - SCOPUS:85097741494
SN - 2162-237X
VL - 32
SP - 3256
EP - 3261
JO - IEEE Transactions on Neural Networks and Learning Systems
JF - IEEE Transactions on Neural Networks and Learning Systems
IS - 7
M1 - 9151329
ER -