Sampled-data fuzzy observer design for nonlinear systems with a nonlinear output equation under measurement quantization

In this paper, a sampled-data fuzzy observer (SDFO) of oscillating nonlinear systems with a nonlinear output equation is proposed based on the Takagi-Sugeno (T-S) fuzzy-model based approach. First, to handle the low transmission capacity of the network, measurements from the system of interest are assumed to be quantized. Next, we employ an exponentially time-varying gain matrix to the SDFO system for enhancing the decay rate performance of the state estimation error dynamics, and these are represented with the T-S fuzzy model. To show better performance on the state estimation, we focus on developing two points: a novel design methodology and a novel looped Lyapunov-Krasovskii functional (LKF). Furthermore, we propose a novel design condition of an SDFO for systems without measurement quantization, which is less conservative than conventional approaches. All of the proposed design conditions are formulated in terms of linear matrix inequalities (LMIs). Finally, appropriate simulation examples are given to validate the effectiveness of the proposed method, and these show better performance compared to the conventional studies. (c) 2021 Elsevier Inc. All rights reserved.

Automated summary

In this paper, a sampled-data fuzzy observer (SDFO) for oscillating nonlinear systems based on the Takagi-Sugeno (T-S) fuzzy-model based approach is proposed. An exponentially time-varying gain matrix is employed to enhance the decay rate performance of the state estimation error dynamics, and the design conditions are formulated in terms of linear matrix inequalities (LMIs). Simulation results demonstrate that the proposed method outperforms conventional studies in terms of state estimation performance.