Niek Doelman

Bio: Niek Doelman is an academic researcher from Leiden University. The author has contributed to research in topics: Adaptive optics & Wavefront. The author has an hindex of 16, co-authored 110 publications receiving 887 citations. Previous affiliations of Niek Doelman include Netherlands Organisation for Applied Scientific Research & Delft University of Technology.

Exploiting the spatiotemporal correlation in adaptive optics using data-driven H2-optimal control

Abstract: A recently proposed data-driven H2-optimal control approach is demonstrated on a laboratory setup. Most adaptive optics (AO) systems are based on a control law that neglects the temporal evolution of the wavefront. The proposed control approach is able to exploit the spatiotemporal correlation in the wavefront without assuming any form of decoupling. By analyzing the dynamic behavior of the wavefront sensor (WFS), it is shown that if the wavefront correction device can be considered static, the transfer function from control input to WFS output reduces to a two-tap impulse response and an integer number of samples delay. Considering this model structure, a data-driven identification procedure is developed to estimate the relevant parameters from measurement data. The specific structure allows for an analytical expression of the optimal controller in terms of the system matrices of the minimum-phase spectral factor of the atmospheric disturbance model. The performance of the optimal controller is compared with that of the standard AO control law. An analysis of the dominant error sources shows that optimal control may reduce the temporal error.

Extremely fast focal-plane wavefront sensing for extreme adaptive optics

Abstract: We present a promising approach to the extremely fast sensing and correction of small wavefront errors in adaptive optics systems. As our algorithm's computational complexity is roughly proportional to the number of actuators, it is particularly suitable to systems with 10,000 to 100,000 actuators. Our approach is based on sequential phase diversity and simple relations between the point-spread function and the wavefront error in the case of small aberrations. The particular choice of phase diversity, introduced by the deformable mirror itself, minimizes the wavefront error as well as the computational complexity. The method is well suited for high-contrast astronomical imaging of point sources such as the direct detection and characterization of exoplanets around stars, and it works even in the presence of a coronagraph that suppresses the diffraction pattern. The accompanying paper in these proceedings by Korkiakoski et al. describes the performance of the algorithm using numerical simulations and laboratory tests.

Turbulence compensation: an overview

Abstract: In general, long range visual detection, recognition and identification are hampered by turbulence caused by atmospheric conditions. Much research has been devoted to the field of turbulence compensation. One of the main advantages of turbulence compensation is that it enables visual identification over larger distances. In many (military) scenarios this is of crucial importance. In this paper we give an overview of several software and hardware approaches to compensate for the visual artifacts caused by turbulence. These approaches are very diverse and range from the use of dedicated hardware, such as adaptive optics, to the use of software methods, such as deconvolution and lucky imaging. For each approach the pros and cons are given and it is indicated for which scenario this approach is useful. In more detail we describe the turbulence compensation methods TNO has developed in the last years and place them in the context of the different turbulence compensation approaches and TNO’s turbulence compensation roadmap. Furthermore we look forward and indicate the upcoming challenges in the field of turbulence compensation.

Magnetic island localization for NTM control by ECE viewed along the same optical path of the ECCD beam

Abstract: Neoclassical tearing modes (NTMs) deteriorate highpressure tokamak plasma confinement and can be suppressed by electron cyclotron current drive (ECCD). In order to obtain efficient suppression, the ECCD power needs to be deposited at the center of an NTM magnetic island. To enhance efficiency, this power also needs to be synchronized in phase with the rotation of the island. The problem is that of real-time detection and precise localization of the island(s) in order to provide the feedback signal required to control the ECCD power deposition area with an accuracy of 1 to 2 cm. Existing schemes based on mode location, equilibrium reconstruction, and plasma profile measurements are limited in positional and temporal accuracy and moreover will become very complex when applied to ITER. To overcome these limitations, it is proposed to provide the feedback signal from electron cyclotron emission (ECE) measurements taken along the identical line of sight as traced by the incident ECCD millimeter-wave beam but in reverse direction. Experiments on TEXTOR have demonstrated a proof of principle. These measurements motivate the further development and the implementation of such an ECCD-aligned ECE system for NTM control in larger fusion machines. Possible implementation of such a system on ASDEX-Upgrade, based on waveguides equipped with a fast directional switch, is presented in this paper. Possible further development for ITER is also discussed.

Iterative Learning Control: Brief Survey and Categorization

Abstract: In this paper, the iterative learning control (ILC) literature published between 1998 and 2004 is categorized and discussed, extending the earlier reviews presented by two of the authors. The papers includes a general introduction to ILC and a technical description of the methodology. The selected results are reviewed, and the ILC literature is categorized into subcategories within the broader division of application-focused and theory-focused results.

Physics of plasmas

Abstract: 1 The Equations of Gas Dynamics 2 Magnetoplasma Dynamics 3 Waves in Magnetoplasmas 4 Magnetoplasma Stability 5 Transport in Magnetoplasmas 6 Extensions of Theory Bibliography Index

Subspace model identification-Part 1. The output-error state-space model identification class of algorithms

Abstract: In this paper, we present two novel algorithms to realize a finite dimensional, linear time-invariant state-space model from input-output data. The algorithms have a number of common features. They are classified as one of the subspace model identification schemes, in that a major part of the identification problem consists of calculating specially structured subspaces of spaces defined by the input-output data. This structure is then exploited in the calculation of a realization. Another common feature is their algorithmic organization: an RQ factorization followed by a singular value decomposition and the solution of an overdetermined set (or sets) of equations. The schemes assume that the underlying system has an output-error structure and that a measurable input sequence is available. The latter characteristic indicates that both schemes are versions of the MIMO Output-Error State Space model identification (MOESP) approach. The first algorithm is denoted in particular as the (elementary MOESP scheme)...

Analysis of Recursive Stochastic Algorithms

Abstract: Recursive algorithms where random observations enter are studied in a fairly general framework. An important feature is that the observations may depend on previous ?outputs? of the algorithm. The considered class of algorithms contains, e.g., stochastic approximation algorithms, recursive identification algorithms, and algorithms for adaptive control of linear systems. It is shown how a deterministic differential equation can be associated with the algorithm. Problems like convergence with probality one, possible convergence points and asymptotic behavior of the algorithm can all be studied in terms of this differential equation. Theorems stating the precise relationships between the differential equation and the algorithm are given as well as examples of applications of the results to problems in identification and adaptive control.