In this study, the vortex-induced vibration (VIV) of a circular cylinder at the low Reynolds number of 200 is simulated by a transient coupled fluid-structure interaction numerical model using the combination of FLUEN...
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In this study, we develop an immersed boundary method - volume of fluid (IBM-VOF) two-phase flow solver to simulate two-phase flow problem contains solid boundaries and free surface and use it to solve the typical pro...
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Iterative learning control can be applied to systems that repeat the same task over a finite duration with resetting to the starting location once each one is complete. The novel feature is the use of information from...
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Iterative learning control can be applied to systems that repeat the same task over a finite duration with resetting to the starting location once each one is complete. The novel feature is the use of information from previous executions of the task in order to update the control signal applied during the next one and thereby sequentially improve performance. Linear iterative learning control laws can be designed using 2D systems theory and recently experimental validation of such designs for single-input single-output examples has been reported. This paper gives the first results on extending this approach to systems with more than one input and output.
In this article a method for the detection of broken rotor bars in asynchronous machines operating under full load is presented. Unlike most Motor Current Signature Analysis (MCSA) approaches, which operate in the fre...
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ISBN:
(纸本)9781479940318
In this article a method for the detection of broken rotor bars in asynchronous machines operating under full load is presented. Unlike most Motor Current Signature Analysis (MCSA) approaches, which operate in the frequency domain, our method operates in the time domain. The scheme is based on the use of a Principal Component Analysis (PCA) fault/anomaly detector. PCA is applied on the three stator currents to subsequently calculate the Q statistic which is employed for detecting the presence/absence of a fault. The efficiency of the proposed scheme was experimentally evaluated using different fault severity levels, ranging from 1/4 of a broken bar to three broken bars. The obtained results indicate that the method can detect the caused asymmetry with a very restricted amount of data.
In this paper, we study a simple X-relay configuration where the shared relay operates in full-duplex (FD) mode. The relay node may have limited spatial degrees of freedom, and as a result, it may not be able to handl...
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In this paper, we study a simple X-relay configuration where the shared relay operates in full-duplex (FD) mode. The relay node may have limited spatial degrees of freedom, and as a result, it may not be able to handle both the loop interference and the multiuser interference. Hence, a decision on the precoding scheme is necessitated. It is often the case that the relay does not have the option of real-time switching between different precoding schemes, either due to hardware limitations of the relay or increased complexity of the problem. Hence, we investigate a “static” precoding decision where the relay node decides on its precoding scheme based only on statistical knowledge of the channel conditions. To perform this decision, the behavior of the system is formulated as a Markov chain and the outage probability of the system is derived in a closed-form with the precoding decision as a parameter. The outage probability is minimized by optimally choosing the precoding scheme, using easily verifiable conditions on the statistical knowledge of the channel conditions. Simulations validate the investigated scheme.
Predicting the motions of rigid objects under contacts is a necessary precursor to planning of robot manipulation of objects. On the one hand physics based rigid body simulations are used, and on the other learning ap...
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ISBN:
(纸本)9781479969357
Predicting the motions of rigid objects under contacts is a necessary precursor to planning of robot manipulation of objects. On the one hand physics based rigid body simulations are used, and on the other learning approaches are being developed. The advantage of physics simulations is that because they explicitly perform collision checking they respect kinematic constraints, producing physically plausible predictions. The advantage of learning approaches is that they can capture the effects on motion of unobservable parameters such as mass distribution, and frictional coefficients, thus producing more accurate predicted trajectories. This paper shows how to bring together the advantages of both approaches to achieve learned simulators of specific objects that outperform previous learning approaches. Our approach employs a fast simplified collision checker and a learning method. The learner predicts trajectories for the object. These are optimised post prediction to minimise interpenetrations according to the collision checker. In addition we show that cleaning the training data prior to learning can also improve performance. Combining both approaches results in consistently strong prediction performance. The new simulator outperforms previous learning based approaches on a single contact push manipulation prediction task. We also present results showing that the method works for multi-contact manipulation, for which rigid body simulators are notoriously unstable.
The paper presents a driver model identification method based on simulator experiments. The visual and vestibular perception of the driver can be modeled by measuring proper signals of the vehicle motion and the envir...
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The paper presents a driver model identification method based on simulator experiments. The visual and vestibular perception of the driver can be modeled by measuring proper signals of the vehicle motion and the environment during the real-time driving of the simulator. The parameters of different drivers are then estimated using a linear difference autoregressive model structure and least-squares estimation techniques. The aim of the identification is to describe the different driver behaviors with the parameters of a driver model with similar structure. The identified driver models are validated by simulation using the same excitation signals as in the simulator experiment and comparing the measured and simulated output of the driver. The main novelty of the paper is the identification method in which a real-time simulator is used.
Modern aircraft increasingly rely on electric power, resulting in high safety criticality and complexity in their electric power generation and distribution systems. Motivated by the resulting rapid increase in the co...
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In this paper, we consider the signal-anticipating behavior in local volt/var control in distribution systems. We define a voltage control game, and show that the signal-anticipating voltage control is the best respon...
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In this paper, we consider the signal-anticipating behavior in local volt/var control in distribution systems. We define a voltage control game, and show that the signal-anticipating voltage control is the best response algorithm of the voltage control game. We further show that the voltage control game has a unique Nash equilibrium, characterize it as the optimum of a global optimization problem, and establish its asymptotic global stability. We then introduce the notion of the price of signal-anticipating (PoSA) to characterize the impact of the signal-anticipating in local voltage control, and use the gap in cost between the network equilibrium in the signal-taking voltage control and the Nash equilibrium in the signal-anticipating voltage control as the metric for PoSA. We characterize how the PoSA scales with the size, topology, and heterogeneity of the power network for a few special cases. We find that the stronger the coupling between different buses is, the larger the PoSA is; the linear network gives the largest PoSA among all possible topologies, but as the size of the network increases, the PoSA will saturate.
This paper deals with the problem of designing an iterative learning control algorithm for discrete linear systems using repetitive process stability theory. The resulting design produces a stabilizing output feedback...
This paper deals with the problem of designing an iterative learning control algorithm for discrete linear systems using repetitive process stability theory. The resulting design produces a stabilizing output feedback controller in the time domain and a feedforward controller that guarantees monotonic convergence in the trial-to-trial domain. The results are also extended to limited frequency range design specification. New design procedure is introduced in terms of linear matrix inequality (LMI) representations, which guarantee the prescribed performances of ILC scheme. A simulation example is given to illustrate the theoretical developments.
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