A general decentralized computational framework for set-valued state estimation and prediction for systems that assume a hybrid state machine representation is introduced in this article. The decentralized scheme consists of a conjunction of a finite set of distributed state machines that are specified by a decomposition of the external signal space. While, in general, the latter is shown to be an outer approximation of the corresponding outcome of the original state machine, here, specific rules for the signal space decomposition are devised by utilizing structural properties of the underyling transition relation, leading to a recovery of the exact state set results. Finally, we illustrate the reduction of the overall computational complexity in a decentralized setting by appyling ℓ-complete approximation representation of the distributed state machines.     «

A general decentralized computational framework for set-valued state estimation and prediction for systems that assume a hybrid state machine representation is introduced in this article. The decentralized scheme consists of a conjunction of a finite set of distributed state machines that are specified by a decomposition of the external signal space. While, in general, the latter is shown to be an outer approximation of the corresponding outcome of the original state machine, here, specific rule...     »