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PROJECT TITLE :

Convergence and Stability of a Constrained Partition-Based Moving Horizon Estimator

ABSTRACT:

A unique type of iterative partition-based moving horizon estimators (PMHE) is proposed, the estimates of which approach those of a centralized moving horizon estimator as the number of iterations will increase. It uses a deterministic setting and will handle known inputs and bounds on the estimated state. We tend to derive conditions on the system, its partitions, and also the scalar regularization parameter, that guarantee convergence towards the optimal centralized state estimate along with stability of the estimation error dynamics, even with a finite range of iterations at every time step. Finally, numerical simulations demonstrate each the options and competitive estimation quality of the proposed methodology.


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