Nonlinear controller design for a magnetic levitation device

Various applications of micro-robotic technology suggest the use of new actuator systems which allow motions to be realized with micrometer accuracy. Conventional actuation techniques such as hydraulic or pneumatic systems are no longer capable of fulfilling the demands of hi-tech micro-scale areas such as miniaturized biomedical devices and MEMS production equipment. These applications pose significantly different problems from actuation on a large scale. In particular, large scale manipulation systems typically deal with sizable friction, whereas micro manipulation systems must minimize friction to achieve submicron precision and avoid generation of static electric fields. Recently, the magnetic levitation technique has been shown to be a feasible actuation method for micro-scale applications. In this paper, a magnetic levitation device is recalled from the authors’ previous work and a control approach is presented to achieve precise motion control of a magnetically levitated object with sub-micron positioning accuracy. The stability of the controller is discussed through the Lyapunov method. Experiments are conducted and showed that the proposed control technique is capable of performing a positioning operation with rms accuracy of 16 μm over a travel range of 30 mm. The nonlinear control strategy proposed in this paper showed a significant improvement in comparison with the conventional control strategies for large gap magnetic levitation systems.     

Extracting the main patterns of natural time series for long-term neurofuzzy prediction

A combination of singular spectrum analysis and locally linear neurofuzzy modeling technique is proposed to make accurate long-term prediction of natural phenomena. The principal components (PCs) obtained from spectral analysis have narrow band frequency spectra and definite linear or nonlinear trends and periodic patterns; hence they are predictable in large prediction horizon. The incremental learning algorithm initiates a model for each of the components as an optimal linear least squares estimation, and adds the nonlinear neurons if they help to reduce error indices over training and validation sets. Therefore, the algorithm automatically constructs the best linear or nonlinear model for each of the PCs to achieve maximum generalization, and the long-term prediction of the original time series is obtained by recombining the predicted components. The proposed method has been primarily tested in long-term prediction of some well-known nonlinear time series obtained from Mackey–Glass, Lorenz, and Ikeda map chaotic systems, and the results have been compared to the predictions made by multi-layered perceptron (MLP) and radial basis functions (RBF) networks. As a real world case study, the method has been applied to the long-term prediction of solar activity where the results have been compared to the long-term predictions of physical precursor and solar dynamo methods.     

Brain functional localization: a survey of image registration techniques

Functional localization is a concept which involves the application of a sequence of geometrical and statistical image processing operations in order to define the location of brain activity or to produce functional/parametric maps with respect to the brain structure or anatomy. Considering that functional brain images do not normally convey detailed structural information and, thus, do not present an anatomically specific localization of functional activity, various image registration techniques are introduced in the literature for the purpose of mapping functional activity into an anatomical image or a brain atlas. The problems addressed by these techniques differ depending on the application and the type of analysis, i.e., single-subject versus group analysis. Functional to anatomical brain image registration is the core part of functional localization in most applications and is accompanied by intersubject and subject-to-atlas registration for group analysis studies. Cortical surface registration and automatic brain labeling are some of the other tools towards establishing a fully automatic functional localization procedure. While several previous survey papers have reviewed and classified general-purpose medical image registration techniques, this paper provides an overview of brain functional localization along with a survey and classification of the image registration techniques related to this problem.     

On the influence of rolling path change on static recrystallization behavior of commercial purity aluminum

An examination of the influence of rolling path change on the static recrystallization behavior of commercial purity aluminum was performed in the present work. Aluminum strips were cold rolled to a reduction of 50 % under various rolling sequences, i.e. single-pass, double-pass from one direction and with reverse directions, and were then annealed in 290 °C for different durations, while mechanical evaluations such as hardness and tensile tests were used to study the mechanical response of cold deformed and annealed samples. It was indicated that a variation in the recrystallization kinetics of the cold rolled aluminum strips takes place when the rolling path is altered from single to double-pass, and from forward to reverse directions. To express the obtained results and provide a more profound understanding of the flow behavior of rolling samples during deformation processing, combined finite element-dislocation models were taken into account, which were capable of predicting the deformation behavior of the aluminum strips and its consequence on the dislocation structure of the deformed metal. Using the proposed mathematical models, it was found that changing the rolling direction and/or sequence may result in the variation of strain field and dislocation density during cold rolling process, consequently affecting the subsequent restoration phenomena.     

Semi-analytical SPICE-compatible ballistic I–V model for 5 nm channel MoS2 FETs

Journal of Computational Electronics - An analytical I–V model for a double-gate ballistic monolayer molybdenum disulfide (MoS2) field-effect transistor (FET) with 5 nm channel...     

Modular Soft Robotic Actuators from Flexible Perforated Sheets

Soft robotic actuators can be designed to achieve complex and tailored motions while simultaneously leveraging their compliance to interact with complex and often delicate environments. Mechanical metamaterials reveal a route to customizable deformations, force exertion, and mechanical energy efficiency attainable by careful arrangement of local geometric features. Herein, modular soft robotic actuators are developed from soft elastomers and flexible thermoplastic sheets of various unit cell designs. The efforts are focused on center-symmetric perforated sheets, which are formed into flexible cylindrical skins that surround the soft inflatable actuators. The results demonstrate the influence of perforation geometry on the spatial stiffness of the reinforcement structure and the proposed actuators’ response through several investigations. It is demonstrated that the free-boundary displacement, maximal force exertion, and mechanical energy efficiency of extensile actuators are dependent on a change of deformation mode in the mesostructure. The spatial stiffness concept is extended to develop soft robotic actuators that can bend, twist, and perform hybrid motions, such as simultaneous bending and twisting. Multisegment soft robotic arms are also developed from the aforementioned actuators. Investigations in this study provide a step toward the development of highly customizable and programmable soft robotic actuators for various applications.     

An embedded β-Ga2O3 layer in a SOI-LDMOS to improve breakdown voltage

Journal of Computational Electronics - In this work, we present a novel silicon-on-insulator (SOI) laterally diffused metal–oxide–semiconductor (LDMOS) using β-Ga2O3 material...