Robust H∞filtering for polynomial nonlinear systems using sum of squares optimization

The robust H_∞ filtering problem for the polynomial nonlinear systems against parametric uncertainty is investigated in this paper. By introducing additional matrix variables, we succeed in eliminating the coupling terms among system dynamics, filter dynamics and the Lyapunov matrix. Sufficient conditions to guarantee the filter stability with guaranteed H_∞ norm from the unknown norm-bounded disturbance signal to the estimation error are derived in terms of state dependent matrix inequalities, which provide an effective way for the application of the new sum of squares programming technique to obtain computationally tractable solutions. Robust H_∞ filter is designed in an efficient computational manner. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed methodology.