Iteratively Improving Moving Horizon Observers for Repetitive Processes

 

Ignacio Alvarado1, Rolf Findeisen, Peter Kühl, Frank Allgöwer and Daniel Limón

University of Stuttgart, 70550 Stuttgart, Germany University of Seville, Seville 41092 , Spain (alvarado,limon)@cartuja.us.es, (findeisen,kuhl,allgower)@ist.uni-stuttgart.de

ABSTRACT. This paper considers the problem of state estimation for repetitive nonlinear systems. Taking the repetitive nature of the process into account a new state estimation scheme is proposed, which from repetition to repetition iteratively improves the estimate. The scheme combines ideas from iterative learning control and moving horizon state estimation. The state estimate during every repetition is based on approximately minimizing the deviation between the measured and estimated output. Stability and iterative improvements of the state estimates are ensured by enforcing a sufficient contraction of the deviation between the measured and estimated output over the considered estimation window. As shown, under the contraction constraints the state estimation scheme ensures asymptotic convergence of the state estimation error in the nominal case, provided that the system satisfies an uniform reconstructability condition.

KEYWORDS: Observers, Repetitive processes, Nonlinear Systems

1. Introduction

Many processes are inherently repetitive, i.e. the same process happens over and over again. Typical examples for repetitively operating processes are:

– Industrial ...

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