Parameter Estimation for Model-Based Sensing of Magneto-Mechanical Resonators

TL;DR

This paper introduces models and real-time estimation methods for magneto-mechanical resonators, enabling efficient and accurate parameter estimation crucial for sensing applications, with significant reductions in computation time.

Contribution

It presents a reference and simplified models for MMR dynamics and inductive readout, along with robust real-time parameter estimation methods, improving speed and maintaining accuracy.

Findings

Simplified models reduce estimation time by up to 100 times.

Methods achieve less than 4% deviation in large deflections.

Effective real-time parameter estimation demonstrated.

Abstract

Magneto-mechanical resonators (MMRs) represent a recently proposed type of passive sensor that enables the estimation of its pose as well as sensing other parameters in its environment. The working principle of MMRs entails an excitation of the sensors by oscillating magnetic fields, followed by a readout process facilitated by inductive receiver coils. The sensing technology relies on real-time parameter estimation. This encompasses the solution of a nonlinear inverse problem, with the induced signals and a suitable forward model as inputs. The aim of this paper is twofold: first, to introduce a reference model and simplified models for the MMR dynamics and inductive readout, and second, to provide robust and real-time capable methods to estimate the model parameters. The effectiveness of the presented methods is evaluated in terms of their real-time potential, precision, and accuracy. All presented methods demonstrate the capacity to estimate the measured signal, with the simplified methods reducing the corresponding parameter estimation time by up to two orders of magnitude at the expense of less than 4 % deviation for large maximum deflection angles.