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Chimera state in complex networks of bistable Hodgkin-Huxley neurons

Physical Review E 2019年第2期100卷 022224-022224页

作者： A. V. Andreev N. S. Frolov A. N. Pisarchik A. E. Hramov Neuroscience and Cognitive Technology Laboratory Center for Technologies in Robotics and Mechatronics Components Innopolis University Universitetskaya 1 Innopolis Republic of Tatarstan 420500 Russia Center for Biomedical Technology Technical University of Madrid Campus Montegancedo 28223 Madrid Spain Saratov State Medical University Bolshaya Kazachia st. 112 Saratov 410012 Russia

In this paper we study a chimera state in complex networks of bistable Hodgkin-Huxley neurons with excitatory coupling, which manifests as a termination of spiking activity of a part of interacting neurons. We provide a detailed investigation of this phenomenon in scale-free, small-world, and random networks and show that the chimera state is robust to the network topology. Nevertheless, network topological properties determine the stability of spatiotemporal states and therefore affect the excitability of the chimera state in the whole network. In particular, the scale-free network whose higher degree nodes are more stable to small perturbations is least exposed to chimera formation and exhibits an abrupt transition from a spiking to a silent regime. On the other hand, small-world and random networks are more likely to provide transitions to the chimera state.

关键词： Chimera states Neuronal networks Neuronal network models

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Mechanics of compliant serial manipulator composed of dual-triangle segments

arXiv

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arXiv 2020年

作者： Zhao, Wanda Klimchik, Alexandr Pashkevich, Anatol Chablat, Damien UMR CNRS 6004 Ecole Centrale Nantes Nantes France Center Technologies in Robotics and Mechatronics Components Innopolis University Tatarstan Russia UMR CNRS 6004 IMT Atlantique Nantes Nantes France Nantes France

The paper focuses on the mechanics of a compliant serial manipulator composed of new type of dual-triangle elastic segments. Both the analytical and numerical methods were used to find the manipulator stable and unstable equilibrium configurations, as well as to predict corresponding manipulator shapes. The stiffness analysis was carried on for both loaded and unloaded modes, the stiffness matrices were computed using the Virtual Joint Method (VJM). The results demonstrate that either buckling or quasi-buckling phenomenon may occur under the loading, if the manipulator corresponding initial configuration is straight or non-straight one. Relevant simulation results are presented that confirm the theoretical study. Copyright © 2020, The Authors. All rights reserved.

关键词： Buckling

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A Globally Convergent State Observer for Multimachine Power Systems with Lossy Lines

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IFAC-PapersOnLine 2020年第2期53卷 5028-5033页

作者： Alexey Bobtsov Romeo Ortega Nikolay Nikolaev Johannes Schiffer Department of Control Systems and Robotics ITMO University Kronverkskiy av. 49 Saint Petersburg 197101 Russia LSS-Supelec 3 Rue Joliot-Curie 91192 Gif-sur-Yvette France Fachgebiet Regelungssysteme und Netzleitechnik Brandenburgische Technische Universität Cottbus – Senftenberg Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia

We present the first solution to the problem of estimation of the state of multimachine power systems with lossy transmission lines. We consider the classical three-dimensional “flux-decay” model of the power system and assume that the active and reactive power as well as the rotor angle and excitation voltage at each generator is available for measurement—a scenario that is feasible with current technology. The design of the observer relies on two recent developments proposed by the authors: a parameter estimation based approach to the problem of state estimation and the use of the dynamic regressor extension and mixing technique to estimate these parameters. Thanks to the combination of these techniques it is possible to overcome the problem of lack of persistent excitation that stymies the application of standard observer designs. Simulation results illustrate the performance of the proposed observer.

关键词： Electric power systems Observers Nonlinear systems Dynamic state estimation

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Stiffness Analysis of Parallel Manipulator NaVaRo with Dual Actuation Modes

Stiffness Analysis of Parallel Manipulator NaVaRo with Dual ...

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International Russian Automation Conference (RusAutoCon)

作者： Alexandr Klimchik Anatol Pashkevich Damien Chablat Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia IMT Atlantique Automation Production and Computer Sciences Nantes France LS2N Centre National de la Recherche Scientifique Nantes France

ISBN: (纸本)9781538649398

The paper deals with enhanced stiffness analysis of the NaVaRo robot, a three-degree-of-freedom planar parallel manipulator with variable actuation schemes that are switched from one to another in order to avoid kinematic singularities. The main scientific contribution is in the area of extension of classical MSA technique for the case of complex open-loop and closed-loop manipulators. The proposed approach produces the Cartesian stiffness matrices and presents the manipulator stiffness model as a set of conventional equations describing the link elasticities that are supplemented by a set of constraints describing connections between links. The main advantage is computational simplicity that straightforwardly aggregates the stiffness model equations avoiding traditional column/row merging procedures of the conventional MSA. The efficiency of this approach is illustrated by comparison analysis of NaVaRo manipulator for different actuation modes.

关键词： Manipulators Mathematical model Legged locomotion Transmission line matrix methods Computational modeling Automation

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Parameter Estimation of Nonlinearly Parameterized Regressions: Application to System Identification and Adaptive Control

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IFAC-PapersOnLine 2020年第2期53卷 1206-1212页

作者： Romeo Ortega Vladislav Gromov Emmanuel Nuño Anton Pyrkin Jose Guadalupe Romero Laboratoire des Signaux et Systémes Supélec CNRS-SUPELEC Gif-sur-Yvette France Faculty of Control Systems and Robotics ITMO University Saint Petersburg Russia Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Department of Computer Science CUCEI University of Guadalajara Guadalajara Mexico Department of Digital Systems ITAM Mexico City Mexico

We propose a solution to the problem of parameter estimation of nonlinearly parameterized regressions—continuous or discrete time—and apply it for system identification and adaptive control. We restrict our attention to parameterizations that can be factorized as the product of two functions, a measurable one and a nonlinear function of the parameters to be estimated. Another feature of the proposed estimator is that parameter convergence is ensured without a persistency of excitation assumption. It is assumed that, after a coordinate change, some of the elements of the transformed function satisfy a monotonicity condition. The proposed estimators are applied to design identifiers and adaptive controllers for nonlinearly parameterized systems, which are traditionally tackled using overparameterization and assuming persistency of excitation.

关键词： Adaptive control systems identification nonlinear control

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Reservoir computing allows recovering hidden network dynamics

Reservoir computing allows recovering hidden network dynamic...

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School on Dynamics of Complex Networks and their Application in Intellectual robotics (DCNAIR)

作者： Artem Badarin Andrey Andreev Neuroscience and Cognitive Technology Laboratory Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Saint Petersburg State University St. Petersburg Russia Baltic Center for Neurotechnology and Artificial Intelligence Immanuel Kant Baltic Federal University Kaliningrad Russia

In this study, we examined reservoir computing (RC)as a tool for predicting the macroscopic dynamics of a subset of oscillators in a network based on the macroscopic dynamics of other parts of it. As a model network, we utilized a network of 300 Kuramoto oscillators with adaptation. Our results demonstrate that reservoir computing effectively addresses this task. Additionally, a similar reservoir computing model was applied to experimental neurovisualization data and exhibited high accuracy in reconstructing damaged EEG channels compared to classical methods like spatial interpolation.

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Stator flux and load torque observers for PMSM ⁎

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IFAC-PapersOnLine 2020年第2期53卷 5051-5056页

作者： Alexey Bobtsov Anton Pyrkin Stanislav Aranovskiy Nikolay Nikolaev Olga Slita Olga Kozachek Dat Vo Quoc ITMO University Kronverksky av. 49 197101 Saint Petersburg Russia Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Equipe Automatique CentaleSupélec - IETR Cesson-Sévigné France ITMO University Kronverksky av. 49 197101 Saint ersburg Russia

In this paper, a stator flux observer and constant load torque estimation methodare developed for non-salient permanent magnet synchronous motors. The methods described here are based on implementation of LTI filters ... 详细信息

关键词： Permanent magnet synchronous motor sensorless control stator flux observer load torque observer

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Quiescence-to-Oscillations Transition Features in Dynamics of Spontaneous Astrocytic Calcium Concentration 20th

Quiescence-to-Oscillations Transition Features in Dynamics o...

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20th International Conference on Mathematical Modeling and Supercomputer technologies, MMST 2020

作者： Sinitsina, Maria S. Gordleeva, Susanna Yu. Kazantsev, Victor B. Pankratova, Evgeniya V. Department of Applied Mathematics Institute of Information Technologies Mathematics and Mechanics Lobachevsky State University of Nizhni Novgorod 23 Gagarin Avenue Nizhny Novgorod603950 Russia Neurotechnology Department Lobachevsky State University of Nizhni Novgorod 23 Gagarin Avenue Nizhny Novgorod603950 Russia Neuroscience and Cognitive Technology Laboratory Center for Technologies in Robotics and Mechatronics Components Innopolis University 1 Universitetskaya Street Innopolis420500 Russia Neuroscience Research Institute Samara State Medical University 89 Chapaevskaya Street Samara443099 Russia

ISBN: (纸本)9783030787585

Recent experimental data show that neural network dynamics significantly depends on the properties of an active nonlinear environment in the brain. Chemical activity of astrocytes is one of the main factor modifying the excitability of the neuronal membrane and regulating the efficiency of signal transmission between neurons. It is known that astrocytes can demonstrate both spontaneous changes in calcium concentration and calcium signals caused by neuronal activity. We focus on spontaneous calcium concentration dynamics in astrocytes. Nonlinear differential equations describing molecular t between neuronsransport in the astrocytes are investigated. Particularly, within the frame of Lavrentovich-Hemkin mathematical model for calcium dynamics, the bifurcation mechanisms of spontaneous calcium concentration change are determined. We show that both soft (emergence of small-amplitude oscillations via supercritical Andronov-Hopf bifurcation) and hard (instantaneous emergence of large-amplitude oscillations via fold limit cycle bifurcation) ways for oscillations emergence can be realized in the astrocyte dynamics. © 2021, Springer Nature Switzerland AG.

关键词： Dynamics

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Finite-time Frequency Estimator for Harmonic Signal

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IFAC-PapersOnLine 2020年第2期53卷 584-589页

作者： Alexey A. Bobtsov Anastasiia O. Vediakova Nikolay A. Nikolaev Olga V. Slita Anton A. Pyrkin Alexey A. Vedyakov Department of Control Systems and Robotics ITMO University Saint Petersburg Russia Laboratory “Control of Complex Systems” Institute of Problems of Mechanical Engineering V.O. Bolshoj pr. 61 St. Petersburg 199178 Russia Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Department of Computer Applications and Systems Saint Petersburg State University Saint Petersburg Russia

This paper is devoted to a frequency estimation of a pure sinusoidal signal in finite-time. The parameterization is based on applying delay operators to a measurable signal. The result is the first-order linear regression model with one parameter, which depends on the signal frequency. The proposed method of finite-time estimation consists of two steps. On the first step, the standard gradient descent method is used to estimate the regression model parameter. On the next step using algebraic equations, finite-time frequency estimate is found. The described method does not require measuring or calculating derivatives of the input signal and uses one integrator for the gradient method and another one for the finite-time estimation. The efficiency of the proposed approach is demonstrated through the set of numerical simulations.

关键词： parameter estimation observers frequency estimation finite-time estimation

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Differences in the resting-state functional brain networks of patients with major depressive disorder and bipolar disorder

Differences in the resting-state functional brain networks o...

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School on Dynamics of Complex Networks and their Application in Intellectual robotics (DCNAIR)

作者： Vladimir Khorev Semen A. Kurkin Rositsa Paunova Drozdstoy Stoyanov Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Baltic Center for Artificial Intelligence and Neurotechnology Immanuel Kant Baltic Federal University Kaliningrad Russia Neurosciences Research Institute Samara State Medical University Samara Russia Plovdiv Medical University Plovdiv Bulgaria

In this work, we analyzed the functional connectivity between different groups of subjects. The groups included patients with major depressive disorder, and patients with bipolar depression that were obtained during experimental research. The data that had been subjected to preprocessing procedures were employed in order to identify the functional connectivity between the brain regions that displayed significant variations.

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