Due to the inherent nonlinear nature of magnetorheological (MR) fluid dampers, one of the challenging aspects for utilizing these devices to achieve high system performance is the development of accurate models and control algorithms that can take advantage of their unique characteristics. In this paper, the direct identification and inverse dynamic modeling for MR fluid dampers using feedforward and recurrent neural networks are studied. The trained direct identification neural network model can be used to predict the damping force of the MR fluid damper on line, on the basis of the dynamic responses across the MR fluid damper and the command voltage, and the inverse dynamic neural network model can be used to generate the command voltage according to the desired damping force through supervised learning. The architectures and the learning methods of the dynamic neural network models and inverse neural network models for MR fluid dampers are presented, and some simulation results are discussed. Finally, the trained neural network models are applied to predict and control the damping force of the MR fluid damper. Moreover, validation methods for the neural network models developed are proposed and used to evaluate their performance. Validation results with different data sets indicate that the proposed direct identification dynamic model using the recurrent neural network can be used to predict the damping force accurately and the inverse identification dynamic model using the recurrent neural network can act as a damper controller to generate the command voltage when the MR fluid damper is used in a semi-active mode.

https://iopscience.iop.org/article/10.1088/0964-1726/14/1/011/pdf

Modeling and control of magnetorheological fluid dampers using neural networks

Semi-active control of buildings and structures with magnetorheological (MR) dampers for earthquake hazard mitigation represents a relatively new research area. In this paper, the Bingham model of MR damper is introduced, and the formula relating the yielding shear stress and the control current of MR dampers is put forward that matches the experimental data. Then an on-line real-time control method for semi-active control of structures with MR dampers is proposed. This method considers the time-delay problem of semi-active control, which can solve distortion of the responses of structures. Finally, through a numerical example of a three-storey reinforced concrete structure, a comparison is made between controlled structure and uncontrolled structure. The calculated results show that MR dampers can reduce the seismic responses of structures effectively. Moreover, the on-line real-time control method is compared with the traditional elastoplastic time-history analysis method, and the efficacy of the on-line real-time control method is demonstrated. In addition, the Levenberg–Marquardt algorithm is used to train the on-line control neural network, and studies show that the algorithm has a very fast convergence rate.

/pdf/semi-active-control-of-structures-incorporated-with-4g7eg2kmz3.pdf

Semi-active control of structures incorporated with magnetorheological dampers using neural networks

In situ study of hydride precipitation kinetics and re-orientation in Zircaloy using synchrotron radiation

The investigation on the prediction of tool wear and the determination of optimum cutting conditions in machining 17-4PH stainless steel

Surface enhancement, the creation of a layer of residual compression at the surface of a component, is widely used to improve the fatigue life in the automotive and aerospace industries. The compressive layer delays fatigue crack initiation and retards small crack propagation. The benefits of surface enhancement are lost if the compressive layer relaxes at the operating temperature of the component. Surface enhancement methods producing minimal cold work are shown to produce the most thermally stable compression. The residual stress and cold work distributions developed in IN718 by shot peening, gravity peening, laser shock peening (LSP) and low plasticity burnishing (LPB) are compared. Estimation of cold work (equivalent true plastic strain) from x-ray diffraction line broadening is described. Thermal relaxation at temperatures ranging from 525C to 670C is correlated to the degree of cold working of the surface, independent of the method of surface enhancement. Highly cold worked (> 15%) shot peened surfaces are found to relax to half the initial level of compression in minutes at all temperatures investigated. The rapid initial relaxation is shown to be virtually independent of either time or temperature from 525C to 670C. The LPB process is described with application to IN718. High cycle fatigue performance after elevated temperature exposure is compared for surfaces treated by LPB and conventional (8A intensity) shot peening.

/pdf/the-effect-of-cold-work-on-the-thermal-stability-of-residual-3z5r8v8ejq.pdf

The Effect of Cold Work on the Thermal Stability of Residual Compression in Surface Enhanced IN718

Smart structure with build-in sensor(s) and actuator(s) that can actively and adaptively change its physical geometry and properties has been considered one of the best candidates in vibration control applications. Implementation of neural networks to system identification and vibration suppression of a smart structure is conducted in this paper. Three neural networks are developed, one for system identification, the second for on-line state estimation, and the third for vibration suppression. It is shown both in analysis and in experiment that these neural networks can identify, estimate, and suppress the vibration of a composite structure by the embedded piezoelectric sensor and actuator. The controller is also shown to be robust to system parameter variations.

Vibration Control of Smart Structures by Using Neural Networks

One of the major difficulties in neural network applications is the selection of the parameters in network configuration and the coefficients in learning rule for fast convergence. This paper develops a network design by combining the Taguchi method and the back-propagation network with an adaptive learning rate for minimum training time and effective vibration suppression. Analyses and experiments show that the optimal design parameters can be determined in a systematic way thereby avoiding the lengthy trial-and-error.

Neural Network Design by Using Taguchi Method

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Vibration Control of Smart Structures by Using Neural Networks

Neural Network Design by Using Taguchi Method