https://puma.ub.uni-stuttgart.de/group/simtech/controlPUMA RSS feed for /group/simtech/control2026-04-23T05:58:51+02:00https://puma.ub.uni-stuttgart.de/bibtex/22bace7d849cb7df0a214c4c9400a9d14/carsten.scherercarsten.scherer2021-12-07T20:40:52+01:00quadratic control imng constraints uncertain integral systems EXC310 pn4 peerReviewed feedforward <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="I. E. Kose" itemprop="url" href="/person/189f278d9dad4c78f153994958230fb15/author/0"><span itemprop="name">I. Kose</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="C. W. Scherer" itemprop="url" href="/person/189f278d9dad4c78f153994958230fb15/author/1"><span itemprop="name">C. Scherer</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="journal">Int. J. Robust Nonlin.</span>, </em> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">19 </span></span>(<span itemprop="issueNumber">11</span>): <span itemprop="pagination">1224-1247</span></em> </span>(<em><span>July 2009<meta content="July 2009" itemprop="datePublished"/></span></em>)</span>Tue Dec 07 20:40:52 CET 2021Int. J. Robust Nonlin.07111224-1247{R}obust ${L}_2$-gain feedforward control of uncertain systems using dynamic {IQC}s192009quadratic control imng constraints uncertain integral systems EXC310 pn4 peerReviewed feedforward We consider the problem of robust L(2)-gain disturbance feedforward control for uncertain systems described in the standard LFT form. We use integral quadratic constraints (IQCs) for describing the uncertainty blocks in the system. For technical reasons related to the feedforward problem, throughout the paper, we work with the duals of the constraints involved in robustness analysis using IQCs. We obtain a convex solution to the problem using a state-space characterization of nominal stability that we have developed recently. Specifically, our solution consists of LMI conditions for the existence of a feedforward controller that guarantees a given L(2)-gain for the closed-loop system. We demonstrate the effectiveness of using dynamic IQCs in robust feedforward design through a numerical example. Copyright (C) 2008 John Wiley & Sons, Ltd.https://puma.ub.uni-stuttgart.de/bibtex/2ca59f2f52bcc5a1a370fa054c25f4acd/carsten.scherercarsten.scherer2021-12-07T20:40:52+01:00forecasts current achievements peerReviewed problems control recent imng trends key theory <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="R. Bars" itemprop="url" href="/person/13d3b2040131f93b9aa77bbada036ebbf/author/0"><span itemprop="name">R. Bars</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="P. Colaneri" itemprop="url" href="/person/13d3b2040131f93b9aa77bbada036ebbf/author/1"><span itemprop="name">P. Colaneri</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="C. E. de Souza" itemprop="url" href="/person/13d3b2040131f93b9aa77bbada036ebbf/author/2"><span itemprop="name">C. de Souza</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="L. Dugard" itemprop="url" href="/person/13d3b2040131f93b9aa77bbada036ebbf/author/3"><span itemprop="name">L. Dugard</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="F. Allgower" itemprop="url" href="/person/13d3b2040131f93b9aa77bbada036ebbf/author/4"><span itemprop="name">F. Allgower</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="A. Kleimenov" itemprop="url" href="/person/13d3b2040131f93b9aa77bbada036ebbf/author/5"><span itemprop="name">A. Kleimenov</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="C. W. Scherer" itemprop="url" href="/person/13d3b2040131f93b9aa77bbada036ebbf/author/6"><span itemprop="name">C. Scherer</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="journal">Annu. Rev. Control</span>, </em> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">30 </span></span>(<span itemprop="issueNumber">1</span>): <span itemprop="pagination">19-30</span></em> </span>(<em><span>2006<meta content="2006" itemprop="datePublished"/></span></em>)</span>Tue Dec 07 20:40:52 CET 2021Annu. Rev. Control119-30{T}heory, algorithms and technology in the design of control systems302006forecasts current achievements peerReviewed problems control recent imng trends key theory Control theory deals with disciplines and methods leading to an automatic decision process in order to improve the performance of a control system. The evolution of control engineering is closely related to the evolution of the technology of sensors and actuators, and to the theoretical controller design methods and numerical techniques to be applied in real-time computing. New control disciplines, new development in the technologies will fertilize quite new control application fields. The status report gives an overview of the current key problems in control theory and design, evaluates the recent major accomplishments and forecasts some new areas. Challenges for future theoretical work are modelling, analysis and design of systems in quite new applications fields. New effective real-time optimal algorithms are needed for 2D and 3D pattern recognition. Design of very large distributed systems has presented a new challenge to control theory including robust control. Control over the networks becomes an important application area. Virtual reality is developing in impressive rate arising new theoretical problems. Distributed hybrid control systems involving extremely large number of interacting control loops, coordinating large number of autonomous agents, handling very large model uncertainties will be in the center of future research. New achievements in bioinformatics will result in new applications. All these challenges need development of new theories, analysis and design methods. (C) 2006 Elsevier Ltd. All rights reserved.https://puma.ub.uni-stuttgart.de/bibtex/250eb44f91950357cf988eb9394027e14/carsten.scherercarsten.scherer2021-12-07T20:40:52+01:00robust multiobjective synthesis control imng feasibility h-infinity inequalities bilinear algorithms formulas linear-systems global h-2 matrix uncertainty output-feedback optimization design parameter peerReviewed lmis structured dynamic order <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="S. Kanev" itemprop="url" href="/person/179b43995f394ecc9dbf6ddc0aa390ca5/author/0"><span itemprop="name">S. Kanev</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="C. W. Scherer" itemprop="url" href="/person/179b43995f394ecc9dbf6ddc0aa390ca5/author/1"><span itemprop="name">C. Scherer</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="M. Verhaegen" itemprop="url" href="/person/179b43995f394ecc9dbf6ddc0aa390ca5/author/2"><span itemprop="name">M. Verhaegen</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="B. De Schutter" itemprop="url" href="/person/179b43995f394ecc9dbf6ddc0aa390ca5/author/3"><span itemprop="name">B. De Schutter</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="journal">Automatica</span>, </em> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">40 </span></span>(<span itemprop="issueNumber">7</span>): <span itemprop="pagination">1115-1127</span></em> </span>(<em><span>July 2004<meta content="July 2004" itemprop="datePublished"/></span></em>)</span>Tue Dec 07 20:40:52 CET 2021Automatica0771115-1127{R}obust output-feedback controller design via local {BMI} optimization402004robust multiobjective synthesis control imng feasibility h-infinity inequalities bilinear algorithms formulas linear-systems global h-2 matrix uncertainty output-feedback optimization design parameter peerReviewed lmis structured dynamic order The problem of designing a globally optimal full-order output-feedback controller for polytopic uncertain systems is known to be a non-convex NP-hard optimization problem, that can be represented as a bilinear matrix inequality optimization problem for most design objectives. In this paper a new approach is proposed to the design of locally optimal controllers. It is iterative by nature, and starting from any initial feasible controller it performs local optimization over a suitably defined non-convex function at each iteration. The approach features the properties of computational efficiency, guaranteed convergence to a local optimum, and applicability to a very wide range of problems. Furthermore, a fast (but conservative) LMI-based procedure for computing an initially feasible controller is also presented. The complete approach is demonstrated on a model of one joint of a real-life space robotic manipulator. (C) 2004 Elsevier Ltd. All rights reserved.https://puma.ub.uni-stuttgart.de/bibtex/2a00dbe6e94fbed05e1f65387e1cfa492/ist_bibist_bib2021-05-18T15:30:24+02:00control;chance stability;Numerical processes;Uncertainty;Stochastic control;randomized Asymptotic stability;Optimization;Predictive predictive control horizon constraints;constrained control;discrete-time stochastic control;Robustness;Stochastic model systems;predictive algorithms;receding <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Matthias Lorenzen" itemprop="url" href="/person/1c14944e4b706b9649e4b475979c37909/author/0"><span itemprop="name">M. Lorenzen</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Fabrizio Dabbene" itemprop="url" href="/person/1c14944e4b706b9649e4b475979c37909/author/1"><span itemprop="name">F. Dabbene</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Roberto Tempo" itemprop="url" href="/person/1c14944e4b706b9649e4b475979c37909/author/2"><span itemprop="name">R. Tempo</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Frank Allgöwer" itemprop="url" href="/person/1c14944e4b706b9649e4b475979c37909/author/3"><span itemprop="name">F. Allgöwer</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="journal">IEEE Trans. Automat. Control</span>, </em> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">62 </span></span>(<span itemprop="issueNumber">7</span>): <span itemprop="pagination">3165-3177</span></em> </span>(<em><span>2017<meta content="2017" itemprop="datePublished"/></span></em>)</span>Tue May 18 15:30:24 CEST 2021IEEE Trans. Automat. Control73165-3177Constraint-Tightening and Stability in Stochastic Model Predictive Control622017control;chance stability;Numerical processes;Uncertainty;Stochastic control;randomized Asymptotic stability;Optimization;Predictive predictive control horizon constraints;constrained control;discrete-time stochastic control;Robustness;Stochastic model systems;predictive algorithms;receding https://puma.ub.uni-stuttgart.de/bibtex/2e23df4e16dd54b13d56b02506f48aca0/ist_bibist_bib2021-05-18T15:30:24+02:00Load frequency control <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="M. Ma" itemprop="url" href="/person/1cc45a0ebe9d1838a4e01440214a7adae/author/0"><span itemprop="name">M. Ma</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="H. Chen" itemprop="url" href="/person/1cc45a0ebe9d1838a4e01440214a7adae/author/1"><span itemprop="name">H. Chen</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="X. Liu" itemprop="url" href="/person/1cc45a0ebe9d1838a4e01440214a7adae/author/2"><span itemprop="name">X. Liu</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="F Allgöwer" itemprop="url" href="/person/1cc45a0ebe9d1838a4e01440214a7adae/author/3"><span itemprop="name">F. Allgöwer</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="journal">Int. J. Electrical Power &amp; Energy Systems</span>, </em> </span>(<em><span>2014<meta content="2014" itemprop="datePublished"/></span></em>)</span>Tue May 18 15:30:24 CEST 2021Int. J. Electrical Power \& Energy Systems289 - 298Distributed model predictive load frequency control of multi-area interconnected power system622014Load frequency control https://puma.ub.uni-stuttgart.de/bibtex/285c6e9c824d74fdf0e755bbb5047f46b/ist_bibist_bib2021-05-18T15:30:24+02:00without predictive control terminal constraints, constrained systems, nonlinear stochastic control, model MPC <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Matthias Lorenzen" itemprop="url" href="/person/14a17018254c269847067be31fd3a6270/author/0"><span itemprop="name">M. Lorenzen</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Matthias A. Müller" itemprop="url" href="/person/14a17018254c269847067be31fd3a6270/author/1"><span itemprop="name">M. Müller</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Frank Allgöwer" itemprop="url" href="/person/14a17018254c269847067be31fd3a6270/author/2"><span itemprop="name">F. Allgöwer</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="journal">Int. J. Robust and Nonlinear Control</span>, </em> </span>(<em><span>2017<meta content="2017" itemprop="datePublished"/></span></em>)</span>Tue May 18 15:30:24 CEST 2021Int. J. Robust and Nonlinear ControlStochastic Model Predictive Control without Terminal Constraints2017without predictive control terminal constraints, constrained systems, nonlinear stochastic control, model MPC https://puma.ub.uni-stuttgart.de/bibtex/2268fd08fb2a0dcc444569615708a033c/ist_bibist_bib2021-05-18T15:30:24+02:00systems. Oscillators;Sociology;Statistics;Controllability;Limit-cycles;Diseases;Orbits;Systems applications;Distributed parameter dynamics;Biological biology;Emerging control systems;Cellular <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="K. Kuritz" itemprop="url" href="/person/1a418015f440cf4db7a1f447fce3dfa94/author/0"><span itemprop="name">K. Kuritz</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="S. Zeng" itemprop="url" href="/person/1a418015f440cf4db7a1f447fce3dfa94/author/1"><span itemprop="name">S. Zeng</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="F. Allgöwer" itemprop="url" href="/person/1a418015f440cf4db7a1f447fce3dfa94/author/2"><span itemprop="name">F. Allgöwer</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="journal">IEEE Control Systems Letters</span>, </em> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">3 </span></span>(<span itemprop="issueNumber">2</span>): <span itemprop="pagination">296-301</span></em> </span>(<em><span>2018<meta content="2018" itemprop="datePublished"/></span></em>)</span>Tue May 18 15:30:24 CEST 2021IEEE Control Systems Letters2296-301Ensemble Controllability of Cellular Oscillators32018systems. Oscillators;Sociology;Statistics;Controllability;Limit-cycles;Diseases;Orbits;Systems applications;Distributed parameter dynamics;Biological biology;Emerging control systems;Cellular https://puma.ub.uni-stuttgart.de/bibtex/269fdb36b26116381977474506b0776b5/ist_bibist_bib2021-05-18T15:30:24+02:00programming;Switches;Convergence;Commutation;Adaptive optimization control;Optimization algorithms;adaptive control algorithms;Optimization;Linear maps;Approximation algorithms;noncommutative convergence;optimisation;derivative-free <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="J. Feiling" itemprop="url" href="/person/122462157c2bb594eb5737c5f1be394ec/author/0"><span itemprop="name">J. Feiling</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="A. Zeller" itemprop="url" href="/person/122462157c2bb594eb5737c5f1be394ec/author/1"><span itemprop="name">A. Zeller</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="C. Ebenbauer" itemprop="url" href="/person/122462157c2bb594eb5737c5f1be394ec/author/2"><span itemprop="name">C. Ebenbauer</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="journal">IEEE Control Systems Letters</span>, </em> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">2 </span></span>(<span itemprop="issueNumber">4</span>): <span itemprop="pagination">743-748</span></em> </span>(<em><span>2018<meta content="2018" itemprop="datePublished"/></span></em>)</span>Tue May 18 15:30:24 CEST 2021IEEE Control Systems Letters4743-748Derivative-Free Optimization Algorithms Based on Non-Commutative Maps22018programming;Switches;Convergence;Commutation;Adaptive optimization control;Optimization algorithms;adaptive control algorithms;Optimization;Linear maps;Approximation algorithms;noncommutative convergence;optimisation;derivative-free https://puma.ub.uni-stuttgart.de/bibtex/213ebc4a8181233a87de80249e0a594eb/mariawirzbergermariawirzberger2020-08-16T17:44:17+02:00distraction feedback myown cognition training attention control <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Maria Wirzberger" itemprop="url" href="/person/18cc82162cfd50441763115ea99d70af4/author/0"><span itemprop="name">M. Wirzberger</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Anastasia Lado" itemprop="url" href="/person/18cc82162cfd50441763115ea99d70af4/author/1"><span itemprop="name">A. Lado</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Lisa Eckerstorfer" itemprop="url" href="/person/18cc82162cfd50441763115ea99d70af4/author/2"><span itemprop="name">L. Eckerstorfer</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Ivan Oreshnikov" itemprop="url" href="/person/18cc82162cfd50441763115ea99d70af4/author/3"><span itemprop="name">I. Oreshnikov</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Jean-Claude Passy" itemprop="url" href="/person/18cc82162cfd50441763115ea99d70af4/author/4"><span itemprop="name">J. Passy</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Adrian Stock" itemprop="url" href="/person/18cc82162cfd50441763115ea99d70af4/author/5"><span itemprop="name">A. Stock</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Amitai Shenhav" itemprop="url" href="/person/18cc82162cfd50441763115ea99d70af4/author/6"><span itemprop="name">A. Shenhav</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Falk Lieder" itemprop="url" href="/person/18cc82162cfd50441763115ea99d70af4/author/7"><span itemprop="name">F. Lieder</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 42nd Annual Conference of the Cognitive Science Society</span>, </em></span><em>page <span itemprop="pagination">1736</span>. </em><em><span itemprop="publisher">Cognitive Science Society</span>, </em>(<em><span>2020<meta content="2020" itemprop="datePublished"/></span></em>)</span>Sun Aug 16 17:44:17 CEST 2020Proceedings of the 42nd Annual Conference of the Cognitive Science Society1736How to navigate everyday distractions: Leveraging optimal feedback to train attention control2020distraction feedback myown cognition training attention control To stay focused on their chosen tasks, people have to inhibit distractions. The underlying attention control skills can improve through reinforcement learning, which can be accelerated by giving feedback. We applied the theory of metacognitive reinforcement learning to develop a training app that gives people optimal feedback on their attention control while they are working or studying. In an eight-day field experiment with 99 participants, we investigated the effect of this training on people’s productivity, sustained attention, and self-control. Compared to a control condition without feedback, we found that participants receiving optimal feedback learned to focus increasingly better (f = .08, p < .01) and achieved higher productivity scores (f = .19, p < .01) during the training. In addition, they evaluated their productivity more accurately (r = .12, p <.01). However, due to asymmetric attrition problems, these findings need to be taken with a grain of salt.https://puma.ub.uni-stuttgart.de/bibtex/2999aacad369b75667ccb969eebea8f2e/mariawirzbergermariawirzberger2020-03-20T16:22:37+01:00myown cognition AI distractions training attention control <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Maria Wirzberger" itemprop="url" href="/person/1c2a484a78f675d7bf62914b062ccd685/author/0"><span itemprop="name">M. Wirzberger</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Ivan Oreshnikov" itemprop="url" href="/person/1c2a484a78f675d7bf62914b062ccd685/author/1"><span itemprop="name">I. Oreshnikov</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Jean-Claude Passy" itemprop="url" href="/person/1c2a484a78f675d7bf62914b062ccd685/author/2"><span itemprop="name">J. Passy</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Anastasia Lado" itemprop="url" href="/person/1c2a484a78f675d7bf62914b062ccd685/author/3"><span itemprop="name">A. Lado</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Amitai Shenhav" itemprop="url" href="/person/1c2a484a78f675d7bf62914b062ccd685/author/4"><span itemprop="name">A. Shenhav</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Falk Lieder" itemprop="url" href="/person/1c2a484a78f675d7bf62914b062ccd685/author/5"><span itemprop="name">F. Lieder</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Digitale Arbeit, digitaler Wandel, digitaler Mensch?. 66. Kongress der Gesellschaft für Arbeitswissenschaft</span>, </em></span><em>page <span itemprop="pagination">C.8.8</span>. </em><em>Berlin, </em><em><span itemprop="publisher">Gesellschaft für Arbeitswissenschaft e.V.</span>, </em>(<em><span>2020<meta content="2020" itemprop="datePublished"/></span></em>)</span>Fri Mar 20 16:22:37 CET 2020BerlinDigitale Arbeit, digitaler Wandel, digitaler Mensch?. 66. Kongress der Gesellschaft für ArbeitswissenschaftC.8.8ACTrain: Ein KI-basiertes Aufmerksamkeitstraining f{\"u}r die Wissensarbeit2020myown cognition AI distractions training attention control https://puma.ub.uni-stuttgart.de/bibtex/2ee24311f62b2704e33bf0feb7bb2069a/karstenkuritzkarstenkuritz2019-02-07T21:34:03+01:00law;ensemble oscillators;Parkinson&#039;s dynamics;biological curve;phase control formulation;cellular rhythms;feedback;neurophysiology;oscillations;oscillators;physiological disease;cancer;heart states;phase distribution;Fourier biology;emerging response system;regulatory behavior;oscillatory populations;phase models;healthy models;ensemble controllability;cellular Papers distributions;population-level mechanism;Oscillators;Sociology;Statistics;Controllability;Limit-cycles;Diseases;Orbits;Systems diseases;phase cancer;cellular feedback systems systems;cellular parameter biophysics;circadian applications;distributed coefficients;oscillating <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="K. Kuritz" itemprop="url" href="/person/1214a67cf1ccf0a66b1cdf33f6cd3a3c3/author/0"><span itemprop="name">K. Kuritz</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="S. Zeng" itemprop="url" href="/person/1214a67cf1ccf0a66b1cdf33f6cd3a3c3/author/1"><span itemprop="name">S. Zeng</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="F. Allgöwer" itemprop="url" href="/person/1214a67cf1ccf0a66b1cdf33f6cd3a3c3/author/2"><span itemprop="name">F. Allgöwer</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="journal">IEEE Control Systems Letters</span>, </em> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">3 </span></span>(<span itemprop="issueNumber">2</span>): <span itemprop="pagination">296-301</span></em> </span>(<em><span>2019<meta content="2019" itemprop="datePublished"/></span></em>)</span>Thu Feb 07 21:34:03 CET 2019IEEE Control Systems Letters2296-301Ensemble Controllability of Cellular Oscillators32019law;ensemble oscillators;Parkinson's dynamics;biological curve;phase control formulation;cellular rhythms;feedback;neurophysiology;oscillations;oscillators;physiological disease;cancer;heart states;phase distribution;Fourier biology;emerging response system;regulatory behavior;oscillatory populations;phase models;healthy models;ensemble controllability;cellular Papers distributions;population-level mechanism;Oscillators;Sociology;Statistics;Controllability;Limit-cycles;Diseases;Orbits;Systems diseases;phase cancer;cellular feedback systems systems;cellular parameter biophysics;circadian applications;distributed coefficients;oscillating https://puma.ub.uni-stuttgart.de/bibtex/24a48320a55144e5de41b8899613eb3e0/mhartmannmhartmann2018-07-20T10:54:15+02:00Feedback control vorlaeufig <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="A. Schmidt" itemprop="url" href="/person/17d76e8038def559e45489301747640a9/author/0"><span itemprop="name">A. Schmidt</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="B. Haasdonk" itemprop="url" href="/person/17d76e8038def559e45489301747640a9/author/1"><span itemprop="name">B. Haasdonk</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="journal">IFAC-PapersOnLine</span>, </em> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">49 </span></span>(<span itemprop="issueNumber">8</span>): <span itemprop="pagination">327 - 332</span></em> </span>(<em><span>2016<meta content="2016" itemprop="datePublished"/></span></em>)<em>2nd IFAC Workshop on Control of Systems Governed by Partial Differential Equations CPDE 2016.</em></span>Fri Jul 20 10:54:15 CEST 2018IFAC-PapersOnLine2nd {IFAC} Workshop on Control of Systems Governed by Partial Differential Equations {CPDE} 20168327 - 332Reduced basis method for {H2} optimal feedback control problems492016Feedback control vorlaeufig Abstract In this paper we examine the application of recently introduced parametric model reduction techniques to the \{H2\} optimal feedback control problem. The \{H2\} control problem provides a realistic framework for control applications, since it considers disturbances in the system and in the measurement outputs. Furthermore it employs state-estimation to reconstruct the unknown state from the noisy measurements. It turns out, that the controller is a dynamical system and two solutions of algebraic Riccati equations (AREs) are required to form it. We apply parametric model order reduction techniques to the \{AREs\} and to the state equation of the observer and show by numerical examples, that this approach can yield a significant speed-up in multi-query scenarios for large scale parametric problems for the control of partial differential equations (PDE).