Robust Mixed <inline-formula> <tex-math notation="TeX">$l_{1}/H_{\infty}$ </tex-math></inline-formula> Filtering for Affine Fuzzy Systems With Measurement Errors
研究了带测量误差的仿射模糊系统的鲁棒混合l1/H∞滤波问题，提出一种模糊基依赖的滤波器设计方法，能降低峰值输出并保证H∞性能，计算负担更小。
This paper investigates the robust filtering problem for a class of nonlinear systems described by affine fuzzy parts with norm-bounded uncertainties. The system outputs are chosen as the premise variables of fuzzy models, and their measured values are chosen as the premise variables and inputs of fuzzy filters. The measurement errors between the outputs of the plant and the inputs of the filter are considered, and as a result, the plant and the estimator cannot always evolve in the same region at the same time, especially in the neighborhoods of region boundaries. By using a piecewise Lyapunov function combined with S-procedure and adding slack matrix variables, a fuzzy-basis-dependent mixed l1/H∞ filter design method is obtained in the formulation of linear matrix inequalities, which allows for reducing the worst case peak output due to the measurement errors, and satisfying an H∞ -norm constraint. In contrast to existing work, the proposed fuzzy-basis-dependent filter can guarantee a better H∞ performance and less computational burden. Finally, a numerical example illustrates the effectiveness of the proposed method.