The aim of this paper is twofold: In the first part, we leverage recent results on scenario design to develop randomized algorithms for approximating the image set of a nonlinear mapping, that is, a (possibly noisy) mapping of a set via a nonlinear function. We introduce minimum-volume approximations which have the characteristic of guaranteeing a low probability of violation, i.e., we admit for a probability that some points in the image set are not contained in the approximating set, but this probability is kept below a pre-specified threshold ε. In the second part of the paper, this idea is then exploited to develop a new family of randomized prediction-corrector filters. These filters represent a natural extension and rapprochement of Gaussian and set-valued filters, and bear similarities with modern tools such as particle filters.
|Original language||English (US)|
|Number of pages||6|
|State||Published - Jul 1 2015|
|Event||8th IFAC Symposium on Robust Control Design, ROCOND 2015 - Bratislava, Slovakia|
Duration: Jul 8 2015 → Jul 11 2015
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering