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Towards improving the estimation performance of a given nonlinear observer: a multi-observer approach

Abstract : Various methods are nowadays available to design observers for broad classes of systems. Nevertheless, the question of the tuning of the observer to achieve satisfactory estimation performance remains largely open. This paper presents a general supervisory design framework for online tuning of the observer gains with the aim of achieving various tradeoffs between robustness and speed of convergence. We assume that a robust nominal observer has been designed for a general nonlinear system and the goal is to improve its performance. We present for this purpose a novel hybrid multi-observer, which consists of the nominal one and a bank of additional observer-like systems, that are collectively referred to as modes and that differ from the nominal observer only in their output injection gains. We then evaluate on-line the estimation cost of each mode of the multi-observer and, based on these costs, we select one of them at each time instant. Two different strategies are proposed. In the first one, initial conditions of the modes are reset each time the algorithm switches between different modes. In the second one, the initial conditions are not reset. We prove a convergence property for the hybrid estimation scheme and we illustrate the efficiency of the approach in improving the performance of a given nominal high-gain observer on a numerical example.
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Contributor : Romain Postoyan Connect in order to contact the contributor
Submitted on : Thursday, September 1, 2022 - 1:36:07 PM
Last modification on : Friday, November 25, 2022 - 6:59:38 PM


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  • HAL Id : hal-03766736, version 1



Elena Petri, Romain Postoyan, Daniele Astolfi, Dragan Nesic, Vincent Andrieu. Towards improving the estimation performance of a given nonlinear observer: a multi-observer approach. 61st IEEE Conference Decision and Control, CDC 2022, Dec 2022, Cancun, Mexico. ⟨hal-03766736⟩



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