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

TL;DR

This paper introduces a hybrid multi-observer framework for online tuning of nonlinear system observers, enhancing estimation performance by adaptively selecting among multiple observer modes based on real-time evaluation.

Contribution

It proposes a novel hybrid multi-observer approach with two switching strategies, improving the tuning and robustness of existing nonlinear observers.

Findings

Convergence of the hybrid estimation scheme is proven.

The approach improves estimation performance in numerical simulations.

Two switching strategies offer flexibility in observer tuning.

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 trade-offs 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.