Robust $L_2$-gain feedforward control of uncertain systems using dynamic IQCs. Int. J. Robust Nonlin., (19)11:1224-1247, July 2009. [PUMA: quadratic control imng constraints uncertain integral systems EXC310 pn4 peerReviewed feedforward] URL
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Robust output-feedback controller design via local BMI optimization. Automatica, (40)7:1115-1127, July 2004. [PUMA: robust 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] URL
Constraint-Tightening and Stability in Stochastic Model Predictive Control. IEEE Trans. Automat. Control, (62)7:3165-3177, 2017. [PUMA: control;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]
Stochastic Model Predictive Control without Terminal Constraints. Int. J. Robust and Nonlinear Control, 2017. [PUMA: without predictive control terminal constraints, constrained systems, nonlinear stochastic control, model MPC]
Ensemble Controllability of Cellular Oscillators. IEEE Control Systems Letters, (3)2:296-301, 2018. [PUMA: systems. Oscillators;Sociology;Statistics;Controllability;Limit-cycles;Diseases;Orbits;Systems applications;Distributed parameter dynamics;Biological biology;Emerging control systems;Cellular]
Distributed model predictive load frequency control of multi-area interconnected power system. Int. J. Electrical Power & Energy Systems, (62):289 - 298, 2014. [PUMA: Load frequency control]
Derivative-Free Optimization Algorithms Based on Non-Commutative Maps. IEEE Control Systems Letters, (2)4:743-748, 2018. [PUMA: programming;Switches;Convergence;Commutation;Adaptive optimization control;Optimization algorithms;adaptive control algorithms;Optimization;Linear maps;Approximation algorithms;noncommutative convergence;optimisation;derivative-free]
How to navigate everyday distractions: Leveraging optimal feedback to train attention control. In Stephanie Denison, Michael Mack, Yang Xu, and Blair C. Armstrong (Eds.), Proceedings of the 42nd Annual Conference of the Cognitive Science Society, 1736, Cognitive Science Society, 2020. [PUMA: distraction feedback myown cognition training attention control] URL
ACTrain: Ein KI-basiertes Aufmerksamkeitstraining für die Wissensarbeit. In Matthias Rötting, and Linda Onnasch (Eds.), Digitale Arbeit, digitaler Wandel, digitaler Mensch?. 66. Kongress der Gesellschaft für Arbeitswissenschaft, C.8.8, Gesellschaft für Arbeitswissenschaft e.V., Berlin, 2020. [PUMA: myown cognition AI distractions training attention control]
Ensemble Controllability of Cellular Oscillators. IEEE Control Systems Letters, (3)2:296-301, 2019. [PUMA: law;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]
Reduced basis method for H2 optimal feedback control problems. IFAC-PapersOnLine, (49)8:327 - 332, 2016. [PUMA: Feedback control vorlaeufig] URL