Two Approaches for Guaranteed State Estimation of Nonlinear Continuous-Time Models

This paper deals with the estimation of the state vector of a nonlinear continuous-time state-space model, such as those frequently encountered in the context of knowledge-based modeling. Unknown and possibly time-varying parameters may be included in an extended state vector to deal with the simultaneous estimation of state and parameters. Observations depending on the (possibly extended) state are assumed to take place at discrete measurement times. Given bounds on the size of the additive measurement errors, guaranteed estimation should then provide bounds on the possible values of the state at any given time. Two recently developed approaches are presented and their performance is compared on a simple test case.

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