Nonlinear State Estimation Using Forward-Backward Propagation of Intervals in an Algorithm

The paper deals with the estimation of the state vector of a discrete-time model from interval output data. When the model outputs are affine in the initial state vector, a number of methods are available to enclose all estimates that are consistent with data by simple sets such as ellipsoids, orthotopes or parallelotopes, thereby providing guaranteed set estimates. In the nonlinear case, the situation is much less developed and there are very few methods that produce such guaranteed estimates. In this paper, the state estimation of a discrete-time model is performed by combining a set-inversion algorithm with a forward-backward propagation of intervals through the model. The resulting methodology is illustrated on an example.