Searched for: subject%3A%22Data%255C-driven%255C+control%22
(1 - 18 of 18)
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Touloujian, Hovsep (author)
Rechargeable Lithium(Li)-Ion Batteries are a ubiquitous element of modern technology, as they pertain to efficient and sustainable energy storage for Electric Vehicles (EVs), as well as wind and solar farms. In the last decades, the production and design of such batteries and their adjacent embedded control, charging, and safety protocols,...
master thesis 2024
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Yin, Lanke (author)
This work introduces a novel training strategy for Gaussian Process (GP) models aimed at improving their predictive accuracy and uncertainty quantification capabilities over extended prediction horizons. This improvement is highly relevant for applications in model predictive control (MPC) in the autonomous driving domain. Learning-based MPC...
master thesis 2024
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Ministeru, Alexandra (author)
Floating Offshore Wind Turbines (FOWTs) pave the way to accessing deep water regions with abundant wind resources that are unreachable to bottom-fixed turbines. This technology is not widely deployed due to the increased cost of producing energy. A suitable control strategy can improve the power quality and extend the lifespan of the FOWT,...
master thesis 2024
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Oomen, T.A.E. (author), Rojas, Cristian R. (author)
A direct data-driven iterative algorithm is developed to accurately estimate the H<sub>∞</sub> norm of a linear time-invariant system from continuous operation, i.e., without resetting the system. The main technical step involves a reversed-circulant matrix that can be evaluated in a model-free setting by performing experiments on the real...
journal article 2024
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Mulders, S.P. (author), Liu, Y. (author), Spagnolo, Fabio (author), Christensen, P.B. (author), van Wingerden, J.W. (author)
Modern industrial wind turbine controllers for partial-load region control are becoming increasingly complex by progressively relying on modeled aerodynamic characteristics. These advanced turbine controllers generally consist of a combined wind speed estimator and tracking controller, allowing for a granular trade-off between energy capture...
journal article 2023
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Guo, M. (author), De Persis, Claudio (author), Tesi, Pietro (author)
We consider data-driven control of input-affine systems via approximate nonlinearity cancellation. Data-dependent semi-definite program is developed to characterize the stabilizer such that the linear dynamics of the closed-loop systems is stabilized and the influence of the nonlinear dynamics is decreased. Because of the additional...
journal article 2023
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Poot, Maurice (author), van Hulst, Jilles (author), Yan, Kai Wa (author), Kostic, Dragan (author), Portegies, Jim (author), Oomen, T.A.E. (author)
The increasing demands on throughput and accuracy of semiconductor manufacturing equipment necessitates accurate feedforward motion control that includes compensation of input nonlinearities. The aim of this paper is to develop a data-driven feedforward approach consisting of a Wiener feedforward, i.e., linear parameterization with an output...
journal article 2023
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van Haren, M. (author), Blanken, L. (author), Oomen, T.A.E. (author)
The increasing demands for motion control result in a situation where Linear Parameter-Varying (LPV) dynamics have to be taken into account. Inverse-model feedforward control for LPV motion systems is challenging, since the inverse of an LPV system is often dynamically dependent on the scheduling sequence. The aim of this paper is to develop...
journal article 2023
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Dinkla, R.T.O. (author), Mulders, S.P. (author), van Wingerden, J.W. (author), Oomen, T.A.E. (author)
In recent years, the amount of data available from systems has drastically increased, motivating the use of direct data-driven control techniques that avoid the need of parametric modeling. The aim of this paper is to analyze closed-loop aspects of these approaches in the presence of noise. To analyze this, a unified formulation of several...
journal article 2023
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Bloemers, Tom (author), Oomen, T.A.E. (author), Toth, Roland (author)
Control of systems with operating condition-dependent dynamics, including control moment gyroscopes (CMGs), often requires operating condition-dependent controllers to achieve high control performance. The aim of this brief is to develop a frequency response data-driven linear parameter-varying (LPV) control design approach for single-input...
journal article 2022
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Li, D. (author), De Schutter, B.H.K. (author)
Data-driven control without using mathematical models is a promising research direction for urban traffic control due to the massive amounts of traffic data generated every day. This article proposes a novel distributed model-free adaptive predictive control (D-MFAPC) approach for multiregion urban traffic networks. More specifically, the...
journal article 2022
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Aarnoudse, Leontine (author), Oomen, T.A.E. (author)
Parameterized feedforward control is at the basis of many successful control applications with varying references. The aim of this paper is to develop an efficient data-driven approach to learn the feedforward parameters for MIMO systems. To this end, a cost criterion is minimized using a stochastic gradient descent algorithm, in which both...
journal article 2022
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Dimanidis, Ioannis (author)
We propose a novel method combining elements of supervised- and Q-learning for the control of dynamical systems subject to unknown disturbances. By using the Inverse Optimization framework and in-hindsight information we can derive a causal parametric optimization policy that approximates a non-causal MPC expert. Furthermore, we propose a new...
master thesis 2021
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Frederik, J.A. (author)
The dissertation discusses different wind turbine control strategies that use pitch actuation to decrease the Levelized Cost of Energy (LCoE) of wind farms. This is achieved by either mitigating the loads experienced by individual turbines, or by enhancing wake mixing in turbines located within a wind farm. The latter strategy exploits the fact...
doctoral thesis 2021
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Skaltsis, Georgios (author)
Data-driven control approaches have been proved highly effective in applications where the dynamics of the system are unknown. The reason is that the use of data in control overcomes the challenge of parametric modeling which requires effort and often leads to an insufficient description of the system’s behavior. By contrast, the behavior of the...
master thesis 2020
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Frederik, J.A. (author), Kröger, Lars (author), Gülker, Gerd (author), van Wingerden, J.W. (author)
A commonly applied method to reduce the cost of wind energy, is alleviating the periodic loads on turbine blades using Individual Pitch Control (IPC). In this paper, a data-driven IPC methodology called Subspace Predictive Repetitive Control (SPRC) is employed. The effectiveness of SPRC will be demonstrated on a scaled 2-bladed wind turbine....
journal article 2018
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Navalkar, S.T. (author)
Wind energy has reached a high degree ofmaturity: for wind-rich onshore locations, it is already competitive with conventional energy sources. However, for low-wind, remote and offshore regions, research efforts are still required to enhance its economic viability. While it is possible to reduce the cost of energy by upscaling wind turbines, it...
doctoral thesis 2016
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Houtzager, I. (author)
Further developments in data-driven control techniques for the load reduction of modern wind turbines can achieve an increased lifetime of components and make the scaling to larger rotor diameters possible, and therefore improve the cost effectiveness of modern wind turbines. Also the success of future rotor designs will heavily depend for their...
doctoral thesis 2011
Searched for: subject%3A%22Data%255C-driven%255C+control%22
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