Regularized Nonlinear Moving-Horizon Observer With Robustness to Delayed and Lost Data

Moving-horizon estimation provides a general method for state estimation with strong theoretical convergence properties under the critical assumption that global solutions are found to the associated nonlinear programming problem at each sampling instant. A particular benefit of the approach is the use of a moving window of data that is used to update the estimate at each sampling instant. This provides robustness to temporary data deficiencies such as lack of excitation and measurement noise, and the inherent robustness can be further enhanced by introducing regularization mechanisms. In this paper, we study moving-horizon estimation in cases when output measurements are lost or delayed, which is a common situation when digitally coded data are received over low-quality communication channels or random access networks. Modifications to a basic moving-horizon state estimation algorithm and conditions for exponential convergence of the estimation errors are given, and the method is illustrated by using a simulation example and experimental data from an offshore oil drilling operation.