Robust Filtering for Discrete - Time Systems withBounded Noise and Parametric

|This paper presents a new approach to nite-horizon guaranteed state prediction for discrete-time systems aaected by bounded noise and unknown-but-bounded parameter uncertainty. Our framework handles possibly nonlinear dependence of the state-space matrices on the uncertain parameters. The main result is that a minimal conndence ellipsoid for the state, consistent with the measured output and the uncertainty description , may be recursively computed in polynomial time, using interior-point methods for convex optimization. With n states, l uncertain parameters appearing linearly in the state-space matrices , with rank-one matrix coeecients, the worst-case complexity grows as O(l(n + l) 3:5). With unstructured uncertainty in all system matrices, the worst-case complexity reduces to O(n 3:5).