Inverse compensation is a fundamental technique in the control of systems with hysteresis. In this expository article, we present algorithms for constructing the inverse of the Preisach operator, a hysteresis model with application to magnetics, smart materials, terrestrial hydrology, and economics. Adaptive inverse control is discussed for cases where hysteresis parameters are not known precisely. To meet the demand of highly dynamic applications, an embedded inversion approach is presented that exploits the parallelism offered by FPGAs.

Control of hysteretic systems through inverse compensation

Magneto-elastoplastic coupling model of ferromagnetic material with plastic deformation under applied stress and magnetic fields

The diagnostic and research aspects of compressor blade fatigue detection have been elaborated in the paper The real maintenance and overhaul problems and characteristic of different modes of metal blade fatigue (LCF, HCF, and VHCF) have been presented The polycrystalline defects and impurities influencing the fatigue, along with their related surface finish techniques, are taken into account The three experimental methods of structural health assessment are considered The metal magnetic memory (MMM), experimental modal analysis (EMA) and tip timing (TTM) methods provide information on the damage of diagnosed objects, for example, compressor blades Early damage symptoms, that is, magnetic and modal properties of material strengthening and weakening phases (change of local dislocation density and grain diameter, increase of structural and magnetic anisotropy), have been described It has been proven that the shape of resonance characteristic gives abilities to determine if fatigue or a blade crack is concerned The capabilities of the methods for steel and titanium alloy blades have been illustrated in examples from active and passive experiments In the conclusion, the MMM, EMA, and TTM have been verified, and the potential for reliable diagnosis of the compressor blades using this method has been confirmed

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High sensitive methods for health monitoring of compressor blades and fatigue detection.

Although the existence of multiple stable phenotypes of living organisms enables random switching between phenotypes as well as non-random history dependent switching called hysteresis, only random switching has been considered in prior experimental and theoretical models of adaptation to variable environments. This work considers the possibility that hysteresis may also evolve together with random phenotype switching to maximize population growth. In addition to allowing the possibility that switching rates between different phenotypes may depend not only on a continuous environmental input variable, but also on the phenotype itself, the present work considers an opportunity cost of the switching events. This opportunity cost arises as a result of a lag phase experimentally observed after phenotype switching and stochastic behavior of the environmental input. It is shown that stochastic environmental variation results in maximal asymptotic growth rate when organisms display hysteresis for sufficiently slowly varying environmental input. At the same time, sinusoidal input does not cause evolution of memory suggesting that the connection between the lag phase, stochastic environmental variation and evolution of hysteresis is a result of a stochastic resonance type phenomenon.

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Hysteresis Can Grant Fitness in Stochastically Varying Environment

Title of Dissertation: QUASI-STATIC CHARACTERIZATION AND MODELING OF THE BENDING BEHAVIOR OF SINGLE CRYSTAL GALFENOL FOR MAGNETOSTRICTIVE SENSORS AND ACTUATORS Supratik Datta, Doctor of Philosophy, 2009 Dissertation Directed By: Professor Alison B. Flatau, Department of Aerospace Engineering Iron-gallium alloys (Galfenol) are structural magnetostrictive materials that exhibit high free-strain at low magnetic fields, high stress-sensitivity and useful thermo-mechanical properties. Galfenol, like smart materials in general, is attractive for use as a dynamic actuator and/or sensor material and can hence find use in active shape and vibration control, real-time structural health monitoring and energy harvesting applications. Galfenol possesses significantly higher yield strength and greater ductility than most smart materials, which are generally limited to use under compressive loads. The unique structural attributes of Galfenol introduce opportunities for use of a smart material in applications that involve tension, bending, shear or torsion. A principal motivation for the research presented in this dissertation is that bending and shear loads lead to development of non-uniform stress and magnetic fields in Galfenol which introduce significantly more complexity to the considerations to be modeled, compared to modeling of purely axial loads. This dissertation investigates the magnetostrictive response of Galfenol under different stress and magnetic field conditions which is essential for understanding and modeling Galfenol’s behavior under bending, shear or torsion. Experimental data are used to calculate actuator and sensor figures of merit which can aid in design of adaptive structures. The research focuses on the bending behavior of Galfenol alloys as well as of laminated composites having Galfenol attached to other structural materials. A four-point bending test under magnetic field is designed, built and conducted on a Galfenol beam to understand its performance as a bending sensor. An extensive experimental study is conducted on Galfenol-Aluminum laminated composites to evaluate the effect of magnetic field, bending moment and GalfenolAluminum thickness ratio on actuation and sensing performance. A generalized recursive algorithm is presented for non-linear modeling of smart structures. This approach is used to develop a magnetomechanical plate model (MMPM) for laminated magnetostrictive composites. Both the actuation and sensing behavior of laminated magnetostrictive composites as predicted by the MMPM are compared with results from existing models and also with experimental data obtained from this research. It is shown that the MMPM predictions are able to capture the non-linear magnetomechanical behavior as well as the structural couplings in the composites. Model simulations are used to predict optimal actuator and sensor design criteria. A parameter is introduced to demarcate deformation regimes dominated by extension and bending. The MMPM results offer significant improvement over existing model predictions by better capturing the physics of the magnetomechanical coupled behavior. QUASI-STATIC CHARACTERIZATION AND MODELING OF THE BENDING BEHAVIOR OF SINGLE CRYSTAL GALFENOL FOR MAGNETOSTRICTIVE SENSORS AND ACTUATORS

Quasi-static characterization and modeling of the bending behavior of single crystal galfenol for magnetostrictive sensors and actuators

IN this admirable book Prof. Ewing has brought together matter which was, before to be found only in the journals of learned societies, and he has also given a full account of his own researches in magnetism. The book is written in a lucid style, and is supplied with numerous references to original papers. Magnetic Induction in Iron and other Metals. By J. A. Ewing (London: Electrician Office.)

E. Wilson

Papers

Magnetic Induction in Iron and other Metals