Dual Set Membership Filter With Minimizing Nonlinear Transformation of Ellipsoid

In this article, we propose a dual set membership filter for nonlinear dynamic systems with additive unknown but bounded noises, and it has three distinct advantages. First, the system is translated into linear by leveraging semi-infinite programming, rather than linearizing function. The programming to find an ellipsoid bounding transformation of ellipsoid, which aims compute tight cover state. Second, duality result derived rigorous analysis; then, first-order Frank–Wolfe method developed efficiently solve lower computation complexity. Third, proposed enjoys stability some special succeeds advantages classic filter. Finally, two illustrative examples in simulations reveal effectiveness