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arXiv 2020年

作者： Delabays, Robin Tyloo, Melvyn Automatic Control Laboratory Swiss Federal Institute of Technology ZurichCH-8092 Switzerland Center for Control Dynamical Systems and Computation UCSB Santa BarbaraCA93106-5070 United States School of Engineering University of Applied Sciences of Western Switzerland HES-SO SionCH-1951 Switzerland LausanneCH-1015 Switzerland

The aim of this manuscript is to present a non-invasive method to recover the network structure of a dynamical system. We propose to use a controlled probing input and to measure the response of the network, in the spirit of what is done to determine oscillation modes in large electrical networks. For a large class of dynamical systems, we show that this approach is analytically tractable and we confirm our findings by numerical simulations of networks of Kuramoto oscillators. Our approach also allows us to determine the number of agents in the network by probing and measuring a single one of them. © 2020, CC BY.

关键词： dynamical systems

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Multi-mode Model Predictive control and Estimation for Uncertain Biotechnological Processes

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IFAC-PapersOnLine 2019年第1期52卷 709-714页

作者： Bruno Morabito Achim Kienle Rolf Findeisen Lisa Carius Laboratory for System Theory and Automatic Control Otto-von-Guericke University Magdeburg Laboratory for Automation/Modelling Otto-von-Guericke University Magdeburg Max Plank Institute for Complex Dynamical Systems Magdeburg

Biotechnological processes are urged to become more flexible and sustainable in order to meet the challenges of modern society. Using renewable feed stocks for the production of value added products is one strategy to achieve sustainability. The challenges arising from the use of renewables are large compared to the commonly used resources. The concentration of nutrients in renewable feed stocks varies between batches and is not optimal in respect to the demands of the cells. The biological system adapts to this situation by changing the metabolic growth modes in dependence of the availability of nutrients in the media. Consequently, the process can run through multiple modes. Each switch of the mode results in a change of the system dynamics which effects the process performance. To overcome the challenges, we propose a model predictive control approach combined with a moving horizon estimator that takes directly the multi-mode nature of the process into account. It ensures optimal performance while guaranteeing that the constraints are met in each phase of the process. The approach is motivated by and applied to a sustainable biopolymer production from juice waste.

关键词： multi-mode model predictive control moving horizon estimation biotechnology state estimation MPC MHE

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Fractional-order biological system: chaos, multistability and coexisting attractors

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The European Physical Journal Special Topics 2021年第5期231卷 1061-1070页

作者： Debbouche, Nadjette Ouannas, Adel Momani, Shaher Cafagna, Donato Pham, Viet-Thanh Laboratory of Dynamical System and Control University of Larbi Ben M’hidi Oum El Bouaghi Algeria Department of Mathematics and Computer Sciences Larbi Ben M’hidi University Oum El Bouaghi Algeria Nonlinear Dynamics Research Center (NDRC) College of Humanities and Sciences Ajman University Ajman UAE Department of Mathematics Faculty of Science University of Jordan Amman Jordan Department of Innovation Engineering University of Salento Lecce Italy Nonlinear Systems and Applications Faculty of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh City Vietnam

In this paper, the nonlinear dynamics of the biological system modeled by the fractional incommensurate order Van der Pol equations are investigated. The stability of the proposed fractional non-autonomous system is analyzed by varying both the fractional order derivative and system parameters. Moreover, very interesting phenomena such as symmetry, multi-stability and coexistence of attractors are discovered in the considered biological system. Numerical simulations are performed by considering the Caputo fractional derivative and results are reported by means of bifurcation diagrams, computation of the largest Lyapunov exponent, phase portraits in 2D and 3D projections.

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Correction: PDMATLAB2D: A Peridynamics MATLAB Two-dimensional Code

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Journal of Peridynamics and Nonlocal Modeling 2024年第3期6卷 530-530页

作者： Seleson, Pablo Pasetto, Marco John, Yohan Trageser, Jeremy Reeve, Samuel Temple Computer Science and Mathematics Division Oak Ridge National Laboratory Oak Ridge USA Department of Mechanical and Aerospace Engineering University of California San Diego La Jolla USA George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology Atlanta USA Center for Control Dynamical Systems and Computation University of California at Santa Barbara Harold Frank Hall Room 5119 Santa Barbara USA Engineering Sciences Center Sandia National Laboratories Albuquerque USA Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak Ridge USA

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Prediction of fitness in bacteria with causal jump dynamic mode decomposition

Prediction of fitness in bacteria with causal jump dynamic m...

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American control Conference

作者： Shara Balakrishnan Aqib Hasnain Nibodh Boddupalli Dennis M. Joshy Robert G. Egbert Enoch Yeung Department of Electrical and Computer Engineering University of California Santa Barbara Department of Mechanical Engineering University of California Santa Barbara Environmental and Biological Sciences Directorate Pacific Northwest National Laboratory Richland WA Mechanical Engineering Center for Control Dynamical Systems and Computation and Biomolecular Science and Engineering University of California Santa Barbara

ISBN: (数字)9781538682661

ISBN: (纸本)9781538682678

In this paper, we consider the problem of learning a predictive model for population cell growth dynamics as a function of the media conditions. We first introduce a generic data-driven framework for training operator-theoretic models to predict cell growth rate. We then introduce the experimental design and data generated in this study, namely growth curves of Pseudomonas putida as a function of casein and glucose concentrations. We use a data driven approach for model identification, specifically the nonlinear autoregressive (NAR) model to represent the dynamics. We show theoretically that Hankel DMD can be used to obtain a solution of the NAR model. We show that it identifies a constrained NAR model and to obtain a more general solution, we define a causal state space system using 1-step, 2-step,..., τ-step predictors of the NAR model and identify a Koopman operator for this model using extended dynamic mode decomposition. The hybrid scheme we call causal-jump dynamic mode decomposition, which we illustrate on a growth profile or fitness prediction challenge as a function of different input growth conditions. We show that our model is able to recapitulate training growth curve data with 96.6% accuracy and predict test growth curve data with 91% accuracy.

关键词： health Growth curves SOLUTIONS(GENERAL) model identification bacteria data driven Pseudomonas putida

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Reconstructing network structures from partial measurements

arXiv

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arXiv 2020年

作者： Tyloo, Melvyn Delabays, Robin Jacquod, Philippe Department of Quantum Matter Physics University of Geneva GenevaCH-1211 Switzerland School of Engineering University of Applied Sciences of Western Switzerland HES-SO SionCH-1951 Switzerland Automatic Control Laboratory ETH Zürich ZürichCH-8092 Switzerland Center for Control Dynamical Systems and Computation UC Santa Barbara Santa BarbaraCA93106-5070 United States

The dynamics of systems of interacting agents is determined by the structure of their coupling network. The knowledge of the latter is therefore highly desirable, for instance to develop efficient control schemes, to accurately predict the dynamics or to better understand inter-agent processes. In many important and interesting situations, the network structure is not known, however, and previous investigations have shown how it may be inferred from complete measurement time series, on each and every agent. These methods implicitly presuppose that, even though the network is not known, all its nodes are. Here we investigate the different problem of inferring network structures within the observed/measured agents. For symmetrically-coupled dynamical systems close to a stable equilibrium, we establish analytically and illustrate numerically that velocity signal correlators encode not only direct couplings, but also geodesic distances in the coupling network, within the subset of measurable agents. When dynamical data are accessible for all agents, our method is furthermore algorithmically more efficient than the traditional ones, because it does not rely on matrix inversion. Copyright © 2020, The Authors. All rights reserved.

关键词： dynamical systems

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Performance–guaranteed consensus control inspired by the mammalian limbic system for a class of nonlinear multi-agents ⁎

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IFAC-PapersOnLine 2020年第2期53卷 9496-9501页

作者： Ignacio Rubio Scola Luis Rodolfo Garcia Carrillo João P. Hespanha Rogelio Lozano CIFASIS (CONICET-UNR) and Department of Mathematics National University of Rosario Rosario 2000 Argentina. Unmanned Systems Laboratory. School of Engineering and Computer Sciences Texas A&M University - Corpus Christi TexasUSA. 78412-5797. Center for Control Dynamical Systems and ComputationUniversity of California Santa Barbara CA 93106 USA. Heuristique et Diagnostic des Systemes Complexes Universit é de Technologie de Compiègne UMR CNRS 7253 60200 CompiegneFrance

Computational models of emotional learning observed in the mammalian brain have inspired diverse self-learning control approaches. These architectures are promising in terms of their fast learning ability and low computational cost. In this paper, the objective is to establish performance–guaranteed emotional learning–inspired control (ELIC) strategies for autonomous multi–agent systems (MAS), where each agent incorporates an ELIC structure to support the consensus controller. The objective of each ELIC structure is to identify and compensate model differences between the theoretical assumptions taken into account when tuning the consensus protocol, and the real conditions encountered in the real system to be stabilized. Stability of the closed-loop MAS is demonstrated using a Lyapunov analysis. Simulation results based on the consensus task of a group of inverted pendulums demonstrate the effectiveness of the proposed ELIC for stabilization of nonlinear MAS.

关键词： Multi-agents systems Biologically-inspired control Robust control Distributed control Nonlinear control

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Locating line and node disturbances in networks of diffusively coupled dynamical agents

arXiv

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arXiv 2020年

作者： Delabays, Robin Pagnier, Laurent Tyloo, Melvyn Automatic Control Laboratory ETH Zurich ZurichCH-8092 Switzerland Center for Control Dynamical Systems and Computation UCSB Santa BarbaraCA93106-5070 United States Department of Mathematics University of Arizona TucsonAZ85721 United States Department of Quantum Matter Physics University of Geneva GenevaCH-1211 Switzerland School of Engineering University of Applied Sciences of Western Switzerland HES-SO SionCH-1951 Switzerland

A wide variety of natural and human-made systems consist of a large set of dynamical units coupled into a complex structure. Breakdown of such systems can have a dramatic impact, as in the case of neurons in the brain or lines in an electric grid, to name but a few. Preventing such catastrophic events requires in particular to be able to detect and locate the source of disturbances as fast as possible. We propose a simple method to identify and locate disturbances in networks of coupled dynamical agents, relying only on time series measurements and on the knowledge of the (possibly Kron-reduced) network structure. The strength and the appeal of the present approach lies in its simplicity paired with the ability to precisely locate disturbances and even to differentiate between line and node disturbances. If we have access to measurement at only a subset of nodes, our method is still able to identify the location of the disturbance if the disturbed nodes are measured. If not, we manage to identify the region of the network where the disturbance occurs. © 2020, CC BY.

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Learning Deep Neural Network Representations for Koopman Operators of Nonlinear dynamical systems

Learning Deep Neural Network Representations for Koopman Ope...

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American control Conference

作者： Enoch Yeung Soumya Kundu Nathan Hodas Biomolecular Science and Engineering Graduate Program Center for Dynamical Systems Control and Computation University of California Santa Barbara Energy and Environment Directorate Pacific Northwest National Laboratory National Security Directorate Pacific Northwest National Laboratory

ISBN: (纸本)9781538679012;9781538679265

The Koopman operator has recently garnered much attention for its value in dynamical systems analysis and data-driven model discovery. However, its application has been hindered by the computational complexity of extended dynamic mode decomposition;this requires a combinatorially large basis set to adequately describe many nonlinear systems of interest, e.g. cyber-physical infrastructure systems, biological networks, social systems, and fluid dynamics. Often the dictionaries generated for these problems are manually curated, requiring domain-specific knowledge and painstaking tuning. In this paper we introduce a computational framework for learning Koopman operators of nonlinear dynamical systems using deep learning. We show that this novel method automatically selects efficient deep dictionaries, requiring much lower dimensional dictionaries while outperforming state-of-the-art methods. We benchmark this method on partially observed nonlinear systems, including the glycolytic oscillator and show it is able to predict on test data quantitatively 100 steps into the future, using only a single timepoint as an initial condition, and quantitative oscillatory behavior 400 steps into the future.

关键词： Nonlinear dynamical systems Dictionaries as Topic dynamical systems System analyses INITIAL CONDITIONS Hydrodynamics complexity classes Learning

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Polarization and fluctuations in signed social networks

arXiv

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arXiv 2019年

作者： Cisneros-Velarde, Pedro Chan, Kevin S. Bullo, Francesco Center for Control Dynamical Systems and Computation University of California Santa Barbara United States U.S. Army Research Laboratory 2800 Powder Mill Rd AdelphisMD20783 United States

Much recent research on social networks has focused on the modeling and analysis of how opinions evolve as a function of interpersonal relationships. It is also of great interest to model and understand the implications of friendly and antagonistic relationships. In this paper, we propose a new, simple and intuitive model that incorporates the socio-psychological phenomenon of the boomerang effect in opinion dynamics. We establish that, under certain conditions on the structure of the signed network that corresponds to the so-called structural balance property, the opinions in the network polarize. Compared to other models in the literature, our model displays a richer and perhaps more intuitive behavior of the opinions when the social network does not satisfy structural balance. In particular, we analyze signed networks in which the opinions show persistent uctuations (including the case of the so-called clustering balance). Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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