Modelling and parameter identification of electromechanical systems for energy harvesting and sensing

Abstract

Advanced modelling of electro-mechanical systems for energy harvesting (EH) and sensing is important to develop reliable self-powered autonomous electronic devices and for structural health monitoring (SHM). In this perspective, a novel computational approach is here proposed for both real-time and off-line parameter identification (PI). The system response is governed by a set of four partial differential equations (PDE) where the three displacement components and the electrical potential are the unknowns. Firstly, the finite element (FE) method is used to reduce the PDE problem into a set of ordinary differential equations (ODE). Then, a statespace model is derived with the aim to limit the PI problem to a subset of unknowns. After that, an identification error is introduced and the Lyapunov theory is used to derive the PI algorithm. The numerical implementation is based on a sensitivity analysis feedback block. The overall proposed computational strategy is robust and results in an exponential asymptotic convergence. The accuracy of the PI method is demonstrated by analysing the time-domain response of an array of piezoelectric bimorphs subjected to low-frequency structural random vibrations. The selected case-study is an existing cable-stayed bridge, for which an extensive dynamic monitoring campaign has provided the experimental data. Once time histories of the device response are obtained through time-dependent dynamic FE simulations, the PI algorithm is used to determine the unknown lumped coefficients of the state-space model. The comparison between FE method and lumped parameters model in terms of tip displacement and output voltage demonstrates the superior predictive capability of the new PI algorithm. As a result of the sensitivity analysis, guidelines to assess the optimal array configuration are also provided.

Identification and Speed Control of DC Motor Using Fractional Order PID: Microcontroller

“…6 The input supply voltage Fig. 7 The output motor angular velocity for the real data By comparison between the exact transfer function in (2) and the obtained estimated one (3), the two equations are very close. The obtained error between the real and estimated data is less than 2% which can be accepted.…”

“…To verify the proposed technique, a well-defined dc motor with the components given in Table1 is utilized. The exact transfer function of this motor is defined and given in (2). In case that an input voltage of 5 V is supplied to the motor as shown in Fig.…”

“…It is dependable, simple to use and having low cost [1]. System identification is the science to construct a mathematical model able of showing the basic properties of a provided system beginning from watched input-output or output-only data [2]. In the past decades, process control has been developed rapidly in the industrial field, and the controller proportional integral derivative (PID) was the most typical one because of its simple structure and strong robustness.…”

Feasibility of energy harvesting from vertical pedestrian‐induced vibrations of footbridges for smart monitoring applications

“…The coupled FE equations of motion for a given domain discretization are given in, for example, Kefal et al. (2019). A classical Rayleigh's approach under the assumption of constant damping ratio for the first two modes is assumed.…”

Comprehensive Analysis of the Energy Harvesting Performance of a Fe-Ga Based Cantilever Harvester in Free Excitation and Base Excitation Mode

“…Accordingly, one needs to make clear the magnitude of magnetization and magnetic flux density caused by stress. There have been several models developed for describing the mathematical relationship between stress and magnetization of magnetostrictive materials, including linear piezomagnetic equation [36], finite element uncoupled and coupled model [37,38], and the distributed-parameter dynamic coupled model [39]. In the harvester proposed in this paper there is a pre-magnetic magnet near the Fe-Ga beam, therefore, it is necessary to consider the effect of a pre-magnetization field on the Villari effect.…”