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Stable IL- 1β-Activation in an Inflammasome Signalling Model Depends on Positive and Negative Feedbacks and Tight Regulation of Protein Production

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IEEE/ACM transactions on computational biology and bioinformatics 2019年第2期16卷 627-637页

作者： Fernando Lopez-Caamal Heinrich J Huber Chemical Engineering Department Universidad de Guanajuato Noria Alta s/n Guanajuato Guanajuato México Institute for Automation Engineering (IFAT) Laboratory for Systems Theory and Automatic Control Otto-von-Guericke University Magdeburg Magdeburg Germany

INTRODUCTION:NLRP3-dependent inflammasome signalling is a key pathway during inflammatory processes and its deregulation is implicated in several diseases. NLRP3-inflammasome pathway activation leads to the rapid, phosphorylation-driven NF$\kappa$κB-pathway signalling, subsequently proceeds via slower transcription/translation process for producing pro-enzymes, and finally leads to the medium-speed enzymatic activation of the central inflammatory mediator IL-$1\beta$1β[1] . We here were interested in how the timing of the rate-limiting step of transcription/translation and the presence of a positive and negative auto-regulation would pose conditions for meaningful and stable IL-$1\beta$1β-activation. METHODS:We extracted the essential topology of the inflammasome pathway network using a linear chain of first-order reaction and a second-order reaction for inhibitory feedback. We then performed an analytical treatment of the resulting ODE set to obtain closed-form formulae. We therefore looked for the steady states and characterized their stability by using a Jacobian-based, local analysis. We employed the Small Gain Theorem from control Theory as recently applied by us [2] and the Gershgorin Circle Theorem to obtain mathematically exact conditions for a positive ON state and stabilities for ON and OFF steady states. RESULTS:We identified an ON- and one OFF- steady state whose properties we characterized in terms of the kinetic parameters by closed-form formulae. We found that under the assumption of a first-order information flow through the network, the existence of a biologically reasonable ON steady state required the simultaneous presence of the positive and the negative feedback. Assuming non-competitivity between IL-$1\beta$1β entities binding to different receptors, we found that a minimum kinetics for protein production is required to sustain a steady state with IL-$1\beta$1β activation. Assuming competitivity between IL-$1\beta$1β entities introduced additio

关键词： Steady State Proteins Biological System Modeling Negative Feedback Production Mathematical Model NLRP 3 Inflammasome IL-1 Z_$\beta$_Z β Interleukin 1 Local Stability Analysis Steady States Stability Positive Feedback Negative Feedback Synthetic Biology

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Decentralized Charging Coordination of Plug-in Electric Vehicles in Distribution Networks With Transformer Aging Considered

Decentralized Charging Coordination of Plug-in Electric Vehi...

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第三十八届中国控制会议

作者： Xu Zhou Yunfeng Shao Suli Zou Zhongjing Ma School of Automation Beijing Institute of Technology The State Grid Lvliang Power Supply Company Automatic Control Laboratory Department of Information Technology and Electrical Engineering Swiss Federal Institute of Technology (ETH)

Coordination algorithms are needed for large-scale plug-in electric vehicles(PEVs) charging problems characterizing battery health and distribution transformer aging. It aims at achieving cost efficient and safe operation without excessive centralization of control decisions. However, the presence of transformers causes coupling among PEVs charging behaviors which brings challenge to make decisions in decentralization. To this end, this paper presents an algorithm based on the so-called alternating direction method of multipliers(ADMM) to coordinate multi-period PEV charging problems amidst the coupling relationship among PEVs charging behaviors. The individual costs include not only nominal PEV charging costs, but also a deviation from the difference between updated auxiliary and dual variables. In a setting where the dual variable is a gradient descent of the augmented Lagrangian multiplier, we prove that the algorithm guarantees convergence to the optimal *** results show the optimality and convergence of the proposed method.

关键词： Plug-in electric vehicles Distribution transformer Hot-spot temperature Battery degradation ADMM Decentralized method

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Preface——Special Issue on New Directions in Nonlinear and Distributed control

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Journal of Systems Science & Complexity 2018年第1期31卷 1-3页

作者： LIU Tengfei XIN Bin JIANG Zhong-Ping State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang 110819 China School of Automation State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing 100081 China Tandon School of Engineering New York University Brooklyn NY 11201 USA

Nonlinearity is ubiquitous in engineering and natural *** development of nonlinear control can be traced back to decades *** date,the research has reached the stage that emphasizes developing methodologies that can ha... 详细信息

关键词：控制问题非线性分布式工程应用程序电子物理自然系统生产线相互作用

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Robust Adaptive Back-stepping Attitude control for Hypersonic Reentry Vehicles

Robust Adaptive Back-stepping Attitude Control for Hypersoni...

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IEEE International Conference on Unmanned Systems (ICUS)

作者： Tiancai Wu Honglun Wang Na Li Yue Yu Jianfa Wu School of Automation Science and Electrical Engineering & Shenyuan Honors College Beihang University Beijing China School of Automation Science and Electrical Engineering & Science and Technology on Aircraft Control Laboratory Beihang University Beijing China Unmanned Systems Research Institute Beihang University Beijing China Beijing Aerospace Automatic Control Institute Beijing China

In this paper, a robust adaptive back-stepping attitude controller is investigated for hypersonic reentry vehicle under unknown parameters and external disturbances. Firstly, the affine nonlinear system of HRV is established in the form of a cascade structure. Secondly, the robust attitude controller based on linear extended state observer, back-stepping method and adaptive control technique is proposed. An adaptive strategy is applied to identify the unknown parameters. Linear extended state observer is applied to observe and compensate the intricate external disturbances. control law and parameter adaptive law can be obtained, and the stability of closed-loop control system can be guaranteed by choosing the appropriate Lyapunov function. Finally, simulation results and discussions are presented to verify the effectiveness of the proposed control strategy.

关键词： Heart rate variability Attitude control Adaptation models Robustness Mathematical model Adaptive systems Observers

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A novel fault classification method based on neural network in smart grid

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International Journal of Electrical engineering 2018年第1期25卷 1-7页

作者： Zhang, Tong Sun, Lanxiang Liu, Jianchang Yu, Haibin College of Information Science and Engineering Northeastern University Shenyang China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control System CAS Shenyang China University of Chinese Academy of Sciences Beijing China

This paper presents a back propagation (BP) neural network method to identify fault types and phases in the smart grid with renewable sources. A fault classification method based on the BP neural network is used to identify patterns of voltage and current measured with phasor measurement unit (PMU). The voltage and current values are decomposed, and then they are used as the input matrix of the BP neural network, which identifies the fault type and fault phasor. All the common short-circuit faults occurred are analyzed, BP neural network method is proposed for fault classification in a power active distribution network. Simulation results show feasibility and usefulness of the BP neural network method, which provides good classification performances.

关键词： Smart power grids

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Robust Stabilization of T-S Fuzzy Stochastic Descriptor Systems via Integral Sliding Modes

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IEEE TRANSACTIONS ON CYBERNETICS 2018年第9期48卷 2736-2749页

作者： Li, Jinghao Zhang, Qingling Yan, Xing-Gang Spurgeon, Sarah K. Institute of Systems Science State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China Instrumentation Control and Embedded Systems Research Group School of Engineering and Digital Arts University of Kent Canterbury U.K. Department of Electronic and Electrical Engineering University College London London U.K.

This paper addresses the robust stabilization problem for T-S fuzzy stochastic descriptor systems using an integral sliding mode control paradigm. A classical integral sliding mode control scheme and a nonparallel distributed compensation (Non-PDC) integral sliding mode control scheme are presented. It is shown that two restrictive assumptions previously adopted developing sliding mode controllers for Takagi-Sugeno (T-S) fuzzy stochastic systems are not required with the proposed framework. A unified framework for sliding mode control of T-S fuzzy systems is formulated. The proposed Non-PDC integral sliding mode control scheme encompasses existing schemes when the previously imposed assumptions hold. Stability of the sliding motion is analyzed and the sliding mode controller is parameterized in terms of the solutions of a set of linear matrix inequalities which facilitates design. The methodology is applied to an inverted pendulum model to validate the effectiveness of the results presented.

关键词： Integral sliding mode control inverted pendulum nonparallel distributed compensation (Non-PDC) robust stabilization Takagi-Sugeno (T-S) fuzzy stochastic descriptor systems

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Coherent H ∞ control for Markovian jump linear quantum systems

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IFAC-PapersOnLine 2020年第2期53卷 269-274页

作者： Yanan Liu Daoyi Dong Ian R. Petersen Qing Gao Steven X. Ding Hidehiro Yonezawa School of Engineering and Information Technology University of New South Wales Canberra ACT 2600 Australia Center for Quantum Computation and Communication Technology Australian Research Council Canberra ACT 2600 Australia Institute for Automatic Control and Complex Systems (AKS) University of Duisburg-Essen. 47057 Duisburg Germany Research School of Electrical Energy and Materials Engineering The Australian National University Canberra ACT 2601 Australia School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment Beijing and also with Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing 100191 China

The purpose of this paper is to design a coherent feedback controller for a Markovian jump linear quantum system suffering from a fault signal. The control objective is to bound the effect of the disturbance input on the output for the time-varying quantum system. We prove the relation between the H ∞ control problem, the dissipation properties, and the solutions of Riccati differential equations, by which the H ∞ controller of the Markovian jump linear quantum system is given by the solutions of Linear Matrix Inequalities (LMIs).

关键词： Coherent quantum feedback H∞ control fault-tolerant control Markovian jump linear quantum systems

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Constrained Gaussian process learning for model predictive control

arXiv

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arXiv 2019年

作者： Matschek, Janine Himmel, Andreas Sundmacher, Kai Findeisen, Rolf Laboratory for Systems Theory and Automatic Control Otto-von-Guericke University Magdeburg Germany Process Systems Engineering Otto-von-Guericke University Magdeburg Germany Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg Germany

Many control tasks can be formulated as a tracking problem of a known or unknown reference signal. Examples are movement compensation in collaborative robotics, the synchronisation of oscillations for power systems or reference tracking of recipes in chemical process operation. Tracking performance as well as guaranteeing stability of the closed loop strongly depends on two factors: Firstly, it depends on whether the future desired tracking reference signal is known and, secondly, whether the system can track the reference at all. This paper shows how to use machine learning, i.e. Gaussian processes, to learn a reference from (noisy) data, while guaranteeing trackability of the modified desired reference predictions in the framework of model predictive control. Guarantees are provided by adjusting the hyperparameters via a constrained optimization. Two specific scenarios, i.e. asymptotically constant and periodical references, are discussed. Copyright © 2019, The Authors. All rights reserved.

关键词： Gaussian distribution

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Learning references with Gaussian processes in model predictive control applied to robot assisted surgery

arXiv

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arXiv 2019年

作者： Matschek, Janine Gonschorek, Tim Hanses, Magnus Elkmann, Norbert Ortmeier, Frank Findeisen, Rolf Laboratory for Systems Theory and Automatic Control Otto-von-Guericke Univ. Magdeburg Germany Software Engineering Otto-von-Guericke Univ. Magdeburg Germany Fraunhofer-Institute for Factory Operation and Automation Magdeburg Germany

One of the key benefits of model predictive control is the capability of controlling a system proactively in the sense of taking the future system evolution into account. However, often external disturbances or references are not a priori known, which renders the predictive controllers shortsighted or uninformed. Adaptive prediction models can be used to overcome this issue and provide predictions of these signals to the controller. In this work we propose to learn references via Gaussian processes for model predictive controllers. To illustrate the approach, we consider robot assisted surgery, where a robotic manipulator needs to follow a learned reference position based on optical tracking measurements. Copyright © 2019, The Authors. All rights reserved.

关键词： Model predictive control

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Developing nonlinear adaptive optimal regulators through an improved neural learning mechanism

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Science China(Information Sciences) 2017年第5期60卷 252-254页

作者： Ding WANG Chaoxu MU The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences Tianjin Key Laboratory of Process Measurement and Control School of Electrical and Information EngineeringTianjin University

Optimal feedback design of dynamical systems is a significant topic in automatic control community and information *** for nonlinear systems,optimal control design always leads to coping with the nonlinear Hamilton-Jacobi-Bellman ***,it is intractable to acquire the analytic solution of the nonlinear Hamilton-JacobiBellman equation for general nonlinear systems.

关键词： Developing nonlinear adaptive optimal regulators through an improved neural learning mechanism

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