PPT-1 Monotone Dynamical Systems: an Introduction

Michael Margaliot School of Electrical Engineering Tel Aviv University Israel Why Study Monotone Systems An easy to check sufficient condition for monotonicity 2 Monotonicity implies strong global results . The coupling is described thr ough communication graph wher each system is node and the contr ol action at each node is only function of its state and the states of its neighbors distrib uted contr ol design method is pr esented which equir es the s 241 Dynamic Systems and Contr ol Lecture 6 Dynamical Systems Readings DD V Chapter Emilio razzoli Aeronautics and str onautics Massachusetts Institute of echnology eb rua ry 23 2011 E razzoli MIT Lecture 6 Dyn L Smith ASU Monotone Dynamical Systems Sontagfest May 23 2011 1 16 brPage 2br Monotone Dynamical System State space metric space with a closed partial order relation Dynamics discretetime or continuoustime semi64258ow 934 Notation 934 conti Dr Rupert Lasser Center for Mathematical Sciences Technische Universit57512at M unchen sina straubwebde Chair of Biomathematics Abstract Monotone dynamical systems which are dynamical systems on an or dered metric space having the property that orde Dashk vskiy Bj orn S uf fer abian R irth Abstract or tw classes of monotone maps on the dimensional positi orthant we sho that or discr ete dynamical system induced by map the origin of is globally asymptotically stable if and only if the map is suc Sistemi Informatica Univ ersit of Florence 50139 Firenze Italy angelidsiunifiit Dep of Mathematics Rutgers Univ ersit New Brunswic k NJ 08901 USA sontagcontrolrutgersedu Summary One of the ey ideas in con trol theory is that of viewing complex dynam Sistemi Informatica Univ ersit of Florence 50139 Firenze Italy angelidsiunifiit Dep of Mathematics Rutgers Univ ersit New Brunswic k NJ 08901 USA sontagcontrolrutgersedu Summary One of the ey ideas in con trol theory is that of viewing complex dynam 1This approis t ewith us, but we claim a greater prision and integrathan isfound in previous work. The idea of bringing together dynacs and informaon theory has rtsin discuis of Maxwe ICM. , Paris, . France. ETH, Zurich, Switzerland. Dynamic. Causal . Modelling. of . fMRI. . timeseries. . Overview. 1 DCM: introduction. 2 Dynamical systems theory. 4 Bayesian inference. . 5 Conclusion. Siwei. . Liu. 1,. Yang Zhou. 1. , Richard Palumbo. 2. , & Jane-Ling Wang. 1. 1. UC Davis; . 2. University of Rhode Island. Motivating Study. Physiological synchrony between romantic partners during nonverbal conditions. Xiaohui XIE. Supervisor: Dr. Hon . Wah. TAM. 2. Outline. Problem background and introduction. Analysis for dynamical systems with time delay. Introduction of dynamical systems. Delayed dynamical systems approach. Andrew Pendergast. Dynamical Systems modeling. Dynamical Systems: Mathematical object to describe behavior that changes over time. Modeling a functional relationship such that time is a primary variable wherein a value or vector function is produced . sparsity. IDM Symposium, April 19, 2016. J. Nathan . Kutz. Department of Applied Mathematics. University of . Washington. Seattle. , WA 98195. -3925. Email: . kutz. @uw.edu. Mathematical Foundations. René Vidal. Center for Imaging Science. Johns Hopkins University. Recognition of individual and crowd motions. Input video. Rigid backgrounds. Dynamic backgrounds. Crowd motions. Group motions. Individual motions.