A New Nonlinear Set Membership Filter Based on Guaranteed Bounding Ellipsoid Algorithm

摘要: Abstract The framework of set membership filter (SMF) with unknown-but-bounded noise assumption provides an attractive alternative for probabilistic filters. However, the potential computational burden and conservation consideration may seriously limit usage this in practical applications. In paper, based on guaranteed bounding ellipsoid approximation, a new enhanced better real-time property reduced is proposed state estimation problem nonlinear systems. model firstly linearized DC programming method used to outer-bound linearization error, which incorporated ellipsoidal approximations. A classical two-step prediction-correction procedure consisting vector sum computation between ellipsoids iterative outer-bounding algorithm intersect strip presented compute feasible estimated states. Simulation results comparisons extended are given demonstrate effectiveness improved performances our algorithm.