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

Abstract

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.