Peak-to-Peak Filtering for Networked Nonlinear DC Motor Systems With Quantization

This paper investigates the peak-to-peak filtering problem for a class of networked nonlinear dc motor systems with quantization. The nonlinear dc motor system is modeled by a Takagi-Sugeno (T-S) fuzzy model. Consider that the measurement output signal and the performance output signal of the system...

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Bibliographic Details
Published in:IEEE transactions on industrial informatics 2018-12, Vol.14 (12), p.5378-5388
Main Authors: Chang, Xiao-Heng, Wang, Yi-Ming
Format: Article
Language:English
Subjects:
Computer simulation
Counters
D C motors
DC motors
Filtration
Fuzzy systems
Linear matrix inequalities
Mathematical analysis
Matrix methods
Measurement
Networked systems
nonlinear dc motor systems
Nonlinear systems
peak-to-peak filtering
Performance indices
Product design
quantization
Quantization (signal)
Resistors
Takagi–Sugeno (T–S) fuzzy systems
Torque
Uncertainty
Voltage control
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Summary:This paper investigates the peak-to-peak filtering problem for a class of networked nonlinear dc motor systems with quantization. The nonlinear dc motor system is modeled by a Takagi-Sugeno (T-S) fuzzy model. Consider that the measurement output signal and the performance output signal of the system are quantized by two static quantizers before being transmitted by the digital communication channel, respectively. Attention is focused on the design of a peak-to-peak filter such that the filtering error system is asymptotically stable and satisfies the prescribed peak-to-peak filtering performance index. Sufficient conditions for such a peak-to-peak filter are expressed in the form of linear matrix inequalities. Finally, an illustrative simulation is given to show the effectiveness of the proposed approach.
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2018.2805707