Identification of structured nonlinear state–space models for hysteretic systems using neural network hysteresis operators

Hysteretic system behavior is ubiquitous in science and engineering fields including measurement systems and applications. In this paper, we put forth a nonlinear state–space system identification method that combines the state–space equations to capture the system dynamics with a compact and exact artificial neural network (ANN) representation of the classical Prandtl–Ishlinskii (PI) hysteresis. These ANN representations called PI hysteresis operator neurons employ recurrent ANNs with classical activation functions, and thus can be trained with classical neural network learning algorithms. The structured nonlinear state–space model class proposed in this paper, for the first time, offers a flexible interconnection of PI hysteresis operators with a linear state–space model through a linear fractional representation. This results in a comprehensive and flexible model structure. The performance is validated both on numerical simulation and on measurement data.</p

From model-driven to data-driven : a review of hysteresis modeling in structural and mechanical systems

Hysteresis is a natural phenomenon that widely exists in structural and mechanical systems. The characteristics of structural hysteretic behaviors are complicated. Therefore, numerous methods have been developed to describe hysteresis. In this paper, a review of the available hysteretic modeling methods is carried out. Such methods are divided into: a) model-driven and b) datadriven methods. The model-driven method uses parameter identification to determine parameters. Three types of parametric models are introduced including polynomial models, differential based models, and operator based models. Four algorithms as least mean square error algorithm, Kalman filter algorithm, metaheuristic algorithms, and Bayesian estimation are presented to realize parameter identification. The data-driven method utilizes universal mathematical models to describe hysteretic behavior. Regression model, artificial neural network, least square support vector machine, and deep learning are introduced in turn as the classical data-driven methods. Model-data driven hybrid methods are also discussed to make up for the shortcomings of the two methods. Based on a multi-dimensional evaluation, the existing problems and open challenges of different hysteresis modeling methods are discussed. Some possible research directions about hysteresis description are given in the final section

Generalized Prandtl-Ishlinskii hysteresis model and its analytical inverse for compensation of hysteresis in smart actuators

Smart actuators such as piezoceramics, magnetostrictive and shape memory alloy actuators, invariably, exhibit hysteresis, which has been associated with oscillations in the open-loop system's responses, and poor tracking performance and potential instabilities of the close-loop system. A number of phenomological operator-based hysteresis models such as the Preisach model, Krasnosel'skii-Pokrovskii model and Prandtl-Ishlinskii model, have been formulated to describe the hysteresis nonlinearities and to seek compensation of the hysteresis effects. Among these, the Prandtl-Ishlinskii model offers greater flexibility and unique property that its inverse can be attained analytically. The Prandtl-Ishlinskii model, however, is limited to rate-independent and symmetric hysteresis nonlinearities. In this dissertation research, the unique flexibility of the Prandtl-Ishlinskii model is explored for describing the symmetric as well as nonlinear hysteresis and output saturation properties of smart actuators, and for deriving an analytical inverse for effective compensation. A generalized play operator with dissimilar envelope functions is proposed to describe asymmetric hysteresis and output saturation nonlinearities of different smart actuators, when applied in conjunction with the classical Prandtl-Ishlinskii model. Dynamic density and dynamic threshold functions of time rate of the input are further proposed and integrated in the classical model to describe rate-dependent symmetric and asymmetric hysteresis properties of smart actuators. A fundamental relationship between the thresholds of the classical and the resulting generalized models is also formulated to facilitate parameters identification. The validity of the resulting generalized Prandtl-Ishlinskii models is demonstrated using the laboratory-measured data for piezoceramic, magnetostrictive and SMA actuators under different inputs over a broad range of frequencies. The results suggest that the proposed generalized models can effectively characterize the rate-dependent as well as rate-independent hysteresis properties of a broad class of smart actuators with output saturation. The properties of the proposed generalized models are subsequently explored to derive its inverse to seek an effective compensator for the asymmetric as well as rate-dependent hysteresis effects. The resulting inverse is applied as a feedforward compensator and simulation results are obtained to demonstrate its effectiveness in compensating the symmetric as well as asymmetric hysteresis of different smart actuators. The effectiveness of the proposed analytical inverse model-based real-time compensator is further demonstrated through its implementation in the laboratory for a piezoceramic actuator. Considering that the generalized Prandtl-Ishlinskii model provides an estimate of the hysteresis properties and the analytical inverse is a hysteresis model, the output of the inverse compensation is expected to yield hysteresis, although of a considerably lower magnitude. The expected compensation error, attributed to possible errors in hysteresis characterization, is analytically derived on the basis of the generalized model and its inverse. The design of a robust controller is presented for a system preceded by the hysteresis effects of an actuator using the proposed error model. The primary purpose is to fuse the analytical inverse compensation error model with an adaptive controller to achieve to enhance tracking precision. The global stability of the chosen control law and the entire closed-loop system is also analytically established. The results demonstrated significantly enhanced tracking performance, when the inverse of the estimated Prandtl-Ishlinskii model is considered in the closed-loop control system

Frequency domain approach for dynamics identification of the actuator with asymmetric hysteresis

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Phase Transitions

As in the past, the workshop brought together researchers with a background in physics, partial differential equations and continuum mechanics and statistical mechanics. Equilibrium and dynamic phase transitions were discussed. A wide range of systems from solid-solid phase transitions to the quantum Curie Weiss model were considered

Nanopositionnement 3D à base de mesure à courant tunnel et piezo-actionnement

The objective of this thesis was to elaborate high performance control strategies and their real-time validation on a tunneling current-based 3D nanopositioning system developed in GIPSA-lab. The thesis lies in the domain of micro-/nano mechatronic systems (MEMS) focused on applications of fast and precise positioning and scanning tunneling microscopy (STM). More precisely, the aim is to position the metallic tunneling tip (like in STM) over the metallic surface using piezoelectric actuators in X, Y and Z directions and actuated micro-cantilever (like in Atomic Force Microscope AFM), electrostatically driven in Z direction, with high precision, over possibly high bandwidth. However, the presence of different adverse effects appearing at such small scale (e.g. measurement noise, nonlinearities of different nature, cross-couplings, vibrations) strongly affect the overall performance of the 3D system. Therefore a high performance control is needed. To that end, a novel 3D model of the system has been developed and appropriate control methods for such a system have been elaborated. First the focus is on horizontal X and Y directions. The nonlinear hysteresis and creep effects exhibited by piezoelectric actuators have been compensated and a comparison between different compensation methods is provided. Modern SISO and MIMO robust control methods are next used to reduce high frequency effects of piezo vibration and cross-couplings between X and Y axes. Next, the horizontal motion is combined with the vertical one (Z axis) with tunneling current and micro-cantilever control. Illustrative experimental results for 3D nanopositioning of tunneling tip, as well as simulation results for surface topography reconstruction and multi-mode cantilever positioning, are finally given.L'objectif de la thèse est l'élaboration de lois de commande de haute performance et leur validation en temps réel sur une plateforme expérimentale 3D de nano-positionnement à base de courant à effet tunnel, développée au laboratoire GIPSA-lab. Elle s'inscrit donc dans le cadre des systèmes micro-/nano-mécatronique (MEMS), et de la commande. Plus précisément, le principal enjeu considéré est de positionner la pointe métallique à effet tunnel (comme en microscopie à effet tunnel STM) contre la surface métallique en utilisant des actionneurs piézoélectriques en X, Y et Z et un micro-levier (comme en microscopie à force atomique AFM) actionné électrostatiquement en Z avec une grande précision et une bande passante élevée. Cependant, la présence de différents effets indésirables apparaissant à cette petite échelle (comme le bruit de mesure, des non-linéarités de natures différentes, les couplages, les vibrations) affectent fortement la performance globale du système 3D. En conséquence, une commande de haute performance est nécessaire. Pour cela, un nouveau modèle 3D du système a été développé et des méthodes de contrôle appropriées pour un tel système ont été élaborées. Tout d'abord l'accent est mis sur de positionnement selon les axes X et Y. Les effets d'hystérésis et de fluage non linéaires présents dans les actionneurs piézoélectriques ont été compensés et une comparaison entre les différentes méthodes de compensation est effectuée. Des techniques modernes de commande robuste SISO et MIMO sont ensuite utilisées pour réduire les effets des vibrations piézoélectriques et des couplages entre les axes X et Y. Le mouvement horizontal est alors combiné avec le mouvement vertical (Axe Z) et une commande du courant tunnel et du micro-levier. Des résultats expérimentaux illustrent le nano positionnement 3D de la pointe, et des résultats de simulation pour la reconstruction de la topographie de la surface ainsi que le positionnement du micro-levier à base d'un modèle multi-modes

Dynamics and Control of Smart Structures for Space Applications

Smart materials are one of the key emerging technologies for a variety of space systems ranging in their applications from instrumentation to structural design. The underlying principle of smart materials is that they are materials that can change their properties based on an input, typically a voltage or current. When these materials are incorporated into structures, they create smart structures. This work is concerned with the dynamics and control of three smart structures: a membrane structure with shape memory alloys for control of the membrane surface flatness, a flexible manipulator with a collocated piezoelectric sensor/actuator pair for active vibration control, and a piezoelectric nanopositioner for control of instrumentation. Shape memory alloys are used to control the surface flatness of a prototype membrane structure. As these actuators exhibit a hysteretic nonlinearity, they need their own controller to operate as required. The membrane structures surface flatness is then controlled by the shape memory alloys, and two techniques are developed: genetic algorithm and proportional-integral controllers. This would represent the removal of one of the main obstacles preventing the use of membrane structures in space for high precision applications, such as a C-band synthetic aperture radar antenna. Next, an adaptive positive position feedback law is developed for control of a structure with a collocated piezoelectric sensor/actuator pair, with unknown natural frequencies. This control law is then combined with the input shaping technique for slew maneuvers of a single-link flexible manipulator. As an alternative to the adaptive positive position feedback law, genetic algorithms are investigated as both system identification techniques and as a tool for optimal controller design in vibration suppression. These controllers are all verified through both simulation and experiments. The third area of investigation is on the nonlinear dynamics and control of piezoelectric actuators for nanopositioning applications. A state feedback integral plus double integral synchronization controller is designed to allow the piezoelectrics to form the basis of an ultra-precise 2-D Fabry-Perot interferometer as the gap spacing of the device could be controlled at the nanometer level. Next, an output feedback linear integral control law is examined explicitly for the piezoelectric actuators with its nonlinear behaviour modeled as an input nonlinearity to a linear system. Conditions for asymptotic stability are established and then the analysis is extended to the derivation of an output feedback integral synchronization controller that guarantees global asymptotic stability under input nonlinearities. Experiments are then performed to validate the analysis. In this work, the dynamics and control of these smart structures are addressed in the context of their three applications. The main objective of this work is to develop effective and reliable control strategies for smart structures that broaden their applicability to space systems

Robust and guaranteed output-feedback force control of piezoelectric actuator under temperature variation and input constraints

This paper addresses the control of manipulation force in a piezoelectric tubeactuator (piezotube) subjected to temperature variation and input constrains.To handle this problem a robust output-feedback design is proposed using aninterval state-space model, which permits consideration of the parameter uncertainties caused by temperature variation. The design method is robust in the sense that the eigenvalues of the interval system are designed to be clustered inside desired regions. For that, an algorithm based on Set Inversion Via IntervalAnalysis (SIVIA) combined with interval eigenvalues computation is proposed. This recursive SIVIA-based algorithm allows to approximate with subpaving the set solutions of the feedback gain[K]that satisfy the inclusion of the eigenvalues of the closed-loop system in the desired region, while at the same time ensuring the control inputs amplitude is bounded by specified saturation. The effectiveness of the control strategy is illustrated by experiments on a real piezotube of which the environmental temperature is varied