893 research outputs found
Adaptive Fractional-Order Sliding Mode Controller with Neural Network Compensator for an Ultrasonic Motor
Ultrasonic motors (USMs) are commonly used in aerospace, robotics, and
medical devices, where fast and precise motion is needed. Remarkably, sliding
mode controller (SMC) is an effective controller to achieve precision motion
control of the USMs. To improve the tracking accuracy and lower the chattering
in the SMC, the fractional-order calculus is introduced in the design of an
adaptive SMC in this paper, namely, adaptive fractional-order SMC (AFOSMC), in
which the bound of the uncertainty existing in the USMs is estimated by a
designed adaptive law. Additionally, a short memory principle is employed to
overcome the difficulty of implementing the fractional-order calculus on a
practical system in real-time. Here, the short memory principle may increase
the tracking errors because some information is lost during its operation.
Thus, a compensator according to the framework of Bellman's optimal control
theory is proposed so that the residual errors caused by the short memory
principle can be attenuated. Lastly, experiments on a USM are conducted, which
comparative results verify the performance of the designed controller.Comment: 9 pages, 9 figure
Learning When to See for Long-term Traffic Data Collection on Power-constrained Devices
Collecting traffic data is crucial for transportation systems and urban
planning, and is often more desirable through easy-to-deploy but
power-constrained devices, due to the unavailability or high cost of power and
network infrastructure. The limited power means an inevitable trade-off between
data collection duration and accuracy/resolution. We introduce a novel
learning-based framework that strategically decides observation timings for
battery-powered devices and reconstructs the full data stream from sparsely
sampled observations, resulting in minimal performance loss and a significantly
prolonged system lifetime. Our framework comprises a predictor, a controller,
and an estimator. The predictor utilizes historical data to forecast future
trends within a fixed time horizon. The controller uses the forecasts to
determine the next optimal timing for data collection. Finally, the estimator
reconstructs the complete data profile from the sampled observations. We
evaluate the performance of the proposed method on PeMS data by an RNN
(Recurrent Neural Network) predictor and estimator, and a DRQN (Deep Recurrent
Q-Network) controller, and compare it against the baseline that uses Kalman
filter and uniform sampling. The results indicate that our method outperforms
the baseline, primarily due to the inclusion of more representative data points
in the profile, resulting in an overall 10\% improvement in estimation
accuracy. Source code will be publicly available.Comment: Accepted by IEEE 26th International Conference on Intelligent
Transportation System
Reshaping of Truncated Pd Nanocubes: Energetic and Kinetic Analysis Integrating Transmission Electron Microscopy with Atomistic-Level and Coarse-Grained Modeling
Stability against reshaping of metallic fcc nanocrystals synthesized with tailored far-from-equilibrium shapes is key to maintaining optimal properties for applications such as catalysis. Yet Arrhenius analysis of experimental reshaping kinetics, and appropriate theory and simulation, is lacking. Thus, we use TEM to monitor the reshaping of Pd nanocubes of ∼25 nm side length between 410 °C (over ∼4.5 h) and 440 °C (over ∼0.25 h), extracting a high effective energy barrier of Eeff ≈ 4.6 eV. We also provide an analytic determination of the energy variation along the optimal pathway for reshaping that involves transfer of atoms across the nanocube surface from edges or corners to form new layers on side {100} facets. The effective barrier from this analysis is shown to increase strongly with the degree of truncation of edges and corners in the synthesized nanocube. Theory matches experiment for the appropriate degree of truncation. In addition, we perform simulations of a stochastic atomistic-level model incorporating a realistic description of diffusive hopping for undercoordinated surface atoms, thereby providing a visualization of the initial reshaping process
Crystal Structure Manipulation of the Exchange Bias in an Antiferromagnetic Film
Exchange bias is one of the most extensively studied phenomena in magnetism,
since it exerts a unidirectional anisotropy to a ferromagnet (FM) when coupled
to an antiferromagnet (AFM) and the control of the exchange bias is therefore
very important for technological applications, such as magnetic random access
memory and giant magnetoresistance sensors. In this letter, we report the
crystal structure manipulation of the exchange bias in epitaxial hcp Cr2O3
films. By epitaxially growing twined (10-10) oriented Cr2O3 thin films, of
which the c axis and spins of the Cr atoms lie in the film plane, we
demonstrate that the exchange bias between Cr2O3 and an adjacent permalloy
layer is tuned to in-plane from out-of-plane that has been observed in (0001)
oriented Cr2O3 films. This is owing to the collinear exchange coupling between
the spins of the Cr atoms and the adjacent FM layer. Such a highly anisotropic
exchange bias phenomenon is not possible in polycrystalline films.Comment: To be published in Scientific Reports, 12 pages, 6 figure
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