Robust $\mathcal {H}_\infty$ Filtering for Vehicle Sideslip Angle With Quantization and Data Dropouts

This paper studies the robust <inline-formula><tex-math notation="LaTeX">$\mathcal {H}_\infty$</tex-math></inline-formula> filtering problem for the in-vehicle networked system with sensor failure, dynamic quantization and data dropouts. The nonlinear vehicle lateral dynamics is described as the Takagi-Sugeno fuzzy system. We assume that the sensor failure is adopted to present inaccurately work of the sensor, and both the measurement and performance output signals are quantized by the dynamic quantizers before being transmitted to the network channel. Moreover, the Bernoulli random binary distribution is considered to describe the data dropouts phenomenon both in the measurement and performance outputs. The proposed filter design method is given in the form of linear matrix inequalities which guarantee that the filtering error system is stochastically stable with <inline-formula><tex-math notation="LaTeX">$\mathcal {H}_\infty$</tex-math></inline-formula> performance index. Finally, the co-simulation of the Matlab/Simulink and Carsim is used to validate the proposed filter design method.