Dutch Network of Systems and Control

Active control of noise propagating through apertures is commonly realized with closed-loop LMS algorithms. However, these algorithms require a large number of error microphones and provide only local attenuation. Slow convergence and high computational effort are additional disadvantages. We propose a wave-domain approach that converges instantaneously, operates with low computational effort and does not require error microphones. It inherently controls sound in all directions in the far-field. The soundfield from the aperture is matched in a least squares sense with the generated soundfield from the loudspeaker array using orthonormal basis functions. Compensation for algorithmic delay, induced by blockwise processing, can be based on microphone placement or signal prediction, at the cost of a loss in attenuation performance. Our simulation results indicate that wave-domain processing has the potential to outperform LMS-based methods in practical active noise control for apertures.

Abstract This paper conveys attitude and rate estimation without rate sensors by performing a critical comparison, validated by extensive simulations. The two dominant approaches to facilitate attitude estimation are based on stochastic and set‐membership reasoning. The first one mostly utilizes the commonly known Gaussian‐approximate filters, namely the EKF and UKF. Although more conservative, the latter seems to be more promising as it considers the inherent geometric characteristics of the underline compact state space and accounts—from first principles—for large model errors. The set‐theoretic approach from a control point of view is addressed, and it is shown that it can overcome reported deficiencies of the Bayesian architectures related to this problem, leading to coordinate‐free optimal filters. Lastly, as an example, a modified predictive filter is derived on the tangent bundle of the special orthogonal group .

In this paper, we consider continuous-time semidecentralized dynamics for the equilibrium computation in a class of aggregative games. Specifically, we propose a scheme where decentralized projected-gradient dynamics are driven by an integral control law. To prove global exponential convergence of the proposed dynamics to an aggregative equilibrium, we adopt a quadratic Lyapunov function argument. We derive a sufficient condition for global convergence that we position within the recent literature on aggregative games, and in particular we show that it improves on established results.

The Learning Analytics and Policy (LAP) workshop explores and documents the ways in which policies at national and regional level are shaping the development of learning analytics. It brings together representatives from around the world who report on the circumstances in their own country. The workshop is preceded by an information gathering phase, and followed by the authoring of a report. The aspirations, achievements and constraints in the different countries are contrasted and documented, providing a valuable resource for the future development of learning analytics.

We provide a new estimator of integral operators with smooth kernels, obtained from a set of scattered and noisy impulse responses. The proposed approach relies on the formalism of smoothing in reproducing kernel Hilbert spaces and on the choice of an appropriate regularization term that takes the smoothness of the operator into account. It is numerically tractable in very large dimensions. We study the estimator's robustness to noise and analyze its approximation properties with respect to the size and the geometry of the dataset. In addition, we show minimax optimality of the proposed estimator.

T.D. and E.K. declare they have no disclosures conflicts of interest to disclose. J.C. is a consultant for Allergan, AMO and Shire.

A crucial challenge in future smart energy grids is the large-scale coordination of distributed energy demand and generation. The well-known PowerMatcher is a promising approach that integrates demand and supply flexibility in the operation of the electricity system through dynamic pricing and a hierarchical bidding coordination scheme. However, as the PowerMatcher focuses on short-term coordination of demand and supply, it cannot fully exploit the flexibility of e.g. electric vehicles over longer periods of time. In this paper, we propose an extension of the PowerMatcher comprising a planning module, which provides coordinated predictions of demand/price over longer times as input to the users for determining their short-term bids. The optimal short-term bidding strategy minimizing a user's costs is then formulated as a Stochastic Dynamic Programming (SDP) problem. We derive an analytic solution for this SDP problem leading to a simple short-term bidding strategy. Numerical results using real-world data show a substantial performance improvement compared to the standard PowerMatcher, without significant additional complexity.

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It is a real challenge for cloud providers to offer cloud resources that are 'virtually unlimited and can be appropriated in any quantity at any time' in a cost-effective way. In this paper, we propose quantitative models that enable cloud providers to make an informed trade-off between cost and quality. Distinguishing between public and private cloud environments we consider infinite and finite source models respectively. In both cases either homogeneous or heterogeneous cloud resource requests are considered. These models can be applied to cloud dimensioning based on request blocking probability, an important SLA parameter. We derive a novel, insightful method that makes it possible to compute resource requirements in private clouds with heterogeneous resource requests. We show the importance of applying finite source models in the context of private clouds. We also use the proposed models to quantify the benefits of cloud federations.