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Wireless Sensor Network-Based distributed Approach to Identify Spatio-Temporal Volterra Model for Industrial distributed parameter systems

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2020年第12期16卷 7671-7681页

作者： Gupta, Saurav Sahoo, Ajit Kumar Sahoo, Upendra Kumar Natl Inst Technol Dept Elect & Commun Engn Rourkela 769008 India

The prodigious amount of data movement among sources, data centers, or processing elements precludes the utilization of least-squares (LS) and fusion-center (FC)-based modeling and control. The LS methods are offline in nature, hence may face difficulty in real-time implementation. FC-based methods that are nonrobust due to single point failure, require large communication bandwidth and computationally fast processing unit. To curb these limitations, this article identifies distributed parameter systems by estimating the parameters of spatio-temporal Volterra model using in-network data processing. It can handle the immense volume of data by distributing the processing tasks of FC among the wireless sensor network nodes. To facilitate distributed optimization, the global objective function is reformulated as a multiple constrained separable problem which is then decomposed into augmented Lagrangian form. Then, alternating direction method of multipliers along with coordinate descent method is employed to obtain the global optimal solution collaboratively. Further, a communication-efficient algorithm is designed for the proposed approach to deploy in an ad-hoc network. Simulations are carried out on two industrial distributed parameter systems (catalytic rod and tubular reactor) to illustrate the practicality of the proposed algorithm.

关键词： Wireless sensor networks Adaptation models Kernel Real-time systems Data models distributed parameter systems Estimation Alternating direction method of multipliers (ADMM) distributed parameter systems (DPSS) Karhunen-Loeve principal component analysis (KL PCA) nonlinear spatio-temporal wireless sensor network (WSN)

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Networked sampled-data control of distributed parameter systems via distributed sensor networks

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COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION 2021年 98卷 105773-105773页

作者： Ji, Huihui Cui, Baotong Liu, Xinzhi Nanjing Audit Univ Sch Math & Stat 86 West Yushan Rd Nanjing 211815 Peoples R China Jiangnan Univ Minist Educ Key Lab Adv Proc Control Light Ind Wuxi 214122 Jiangsu Peoples R China Jiangnan Univ Sch IoT Engn Wuxi 214122 Jiangsu Peoples R China Univ Waterloo Dept Appl Math Waterloo ON N2L 3G1 Canada

This paper studies the problem of the distributed networked sampled-data control for a class of distributed parameter systems with spatially-dependent diffusion term. In view of limited fixed sampling spatial points, the distributed sensor network is proposed to obtain the sampled-data measurements of the state which can efficaciously circumvent blind sampling area or sampling error. A distributed sampled data output feedback controller based on the distributed sensor network is designed to ensure the stabilization of the distributed parameter systems with the time delays induced by the communication network. Based on Lyapunov method, linear matrix inequality technique and time-delay approach, the global exponential stability criteria are obtained for the closed-loop distributed parameter systems under three different boundary conditions, respectively. Finally, the numerical simulation proves the effectiveness of the controller, and numerical comparison shows that the proposed control method is less conservative. (c) 2021 Elsevier B.V. All rights reserved.

关键词： distributed parameter systems Sampled-data control Sensor network Time delay Exponential stability and stabilization

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Sampled-Data Control of distributed parameter systems via an Event-Based Communication Scheme

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IEEE ACCESS 2021年 9卷 139565-139575页

作者： Zhu, Jianfeng Yangtze Normal Univ Sch Management Chongqing 408100 Peoples R China

This paper studies the problem of event-triggered control for a class of networked distributed parameter systems with Markov jump parameters. To reduce the number of packages transmitted over the communication network, an adaptive event-triggered mechanism is introduced. The Galerkin method is employed to obtain the nonlinear ordinary differential equation systems, which can accurately describe the dynamics of the dominant modes of the considered distributed parameter systems. The systems are subsequently parameterized by a multilayer neural network with one-hidden layer and zero bias terms, and the linear ordinary differential equation systems are derived. Then, Lyapunov approach is used to analyze stability of the considered systems, and by employing the strong law of large numbers and Gronwall inequality technique, almost surely exponential stability condition is derived. Moreover, a linear sampled-data-based controller is designed to stabilize the closed-loop systems. Finally, a practical example is shown to demonstrate the effectiveness of the achieved theoretical results.

关键词： Markov processes Method of moments Eigenvalues and eigenfunctions Artificial neural networks Adaptive systems Nonhomogeneous media Switches distributed parameter systems Markov jump parameters Galerkin method neural model adaptive event-triggered networked control almost surely exponential stability

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On local exponential stability of equilibrium profiles of nonlinear distributed parameter systems ⁎ ⁎

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IFAC-PapersOnLine 2021年第9期54卷 390-396页

作者： A. Hastir J.J. Winkin D. Dochain University of Namur Department of Mathematics and Namur Institute for Complex Systems (naXys) Rue de Bruxelles 61 B-5000 Namur Belgium Université Catholique de Louvain Institute of Information and Communication Technologies Electronics and Applied Mathematics (ICTEAM) Avenue Georges Lemaitre 4-6 B-1348 Louvain-La-Neuve Belgium

Local exponential (exp.) stability of nonlinear distributed parameter, i.e. infinite-dimensional state space, systems is considered. A weakened concept of Fréchet differentiability (( Y,X )-Fréchet differentiability) for nonlinear operators defined on Banach spaces is proposed, including the introduction of an alternative space (Y) in the analysis. This allows more freedom in the manipulation of norm-inequalities leading to adapted Fréchet differentiability conditions that are easier to check. Then, provided that the nonlinear semigroup generated by the nonlinear dynamics is Fréchet-differentiable in the new sense, appropriate local exp. stability of the equilibria for the nonlinear system is established. In particular, the nonlinear semigroup has to be Fréchet differentiable on Y and (Y,X)-Fréchet differentiable in order to go back to the original state space X. This approach may be called ”perturbation-based” since exp. stability is also deduced from exp. stability of a linearized version of the nonlinear semigroup. Under adapted Fréchet differentiability assumptions, the main result establishes that local exp. stability of an equilibrium for the nonlinear system is guaranteed as long as the exp. stability holds for the linearized semigroup. The same conclusion holds regarding instability. The theoretical results are illustrated on a convection-diffusion-reaction system.

关键词： distributed parameter systems Nonlinear systems Equilibrium Exponential stability

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Iterative learning control tracks the slowly varying reference trajectory of linear parabolic distributed parameter systems

Iterative learning control tracks the slowly varying referen...

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Chinese Automation Congress (CAC)

作者： Xisheng Dai Jing Wu Fan Zhang Senping Tian School of Electrical Electronics and Computer Science Guangxi University of Science and Technology Liuzhou P. R. China School of Automation Science and Engineering South China University of Technology Guangzhou P. R. China

ISBN: (数字)9781665426473

ISBN: (纸本)9781665426480

Up to now, based upon the existing results on iterative learning control (ILC) of distributed parameter systems have not been fully utilized for the variable reference trajectory case. This paper proposes a new ILC algorithm for a parabolic distributed parameter system that assumes that the reference trajectory changes slowly with the number of iterations. The sufficient conditions are established such that the tracking error convergent to a bounded domain. And the detail theoretical analysis of the algorithm convergence is also presented clearly under some given conditions. Numerical examples verify the effectiveness of the proposed method.

关键词： Sufficient conditions Automation Trajectory distributed parameter systems Iterative learning control Convergence

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Stabilization for Markovian Jump distributed parameter systems With Time Delay

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IEEE ACCESS 2019年 7卷 103931-103937页

作者： Li, Yanbo Chen, Chaoyang Wang, Baoxian Guangxi Univ Finance & Econ Sch Informat & Stat Nanning 530001 Peoples R China Hunan Univ Sci & Technol Sch Informat & Elect Engn Xiangtan 411201 Peoples R China Boston Univ Ctr Polymer Studies Boston MA 02215 USA Boston Univ Dept Phys 590 Commonwealth Ave Boston MA 02215 USA China Three Gorges Univ Coll Sci Yichang 443002 Peoples R China

The problems of stochastic stability and stabilization for a class of Markovian jump distributed parameter systems with time delay are researched in this paper. First, taking advantage of a combination of Poincare inequality and Green formula, a stochastic stability criterion is presented by a linear matrix inequality (LMI) approach. Then, a state feedback controller is designed. Based on the proposed results, the sufficient conditions of the close-loop system's stochastic stability are given in terms of a set of LMIs by constructing the appropriate Lyapunov functionals, calculating the weak infinitesimal generator, and using the Schur complement lemma. The sufficient conditions could be solved directly and applied to engineering practice conveniently. The obtained results generalize and enrich the theory of distributed parameter systems with time delay. The model of Markovian jump distributed parameter systems is more fitting the actual system's requirements and has wider application scope. Finally, numerical examples are used to demonstrate the validity of the method.

关键词： Markovian jump distributed parameter systems stochastic stability linear matrix inequality

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Sampled-data fuzzy control for a class of nonlinear parabolic distributed parameter systems under spatially point measurements

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FUZZY SETS AND systems 2019年 374卷 60-81页

作者： Wang, Zi-Peng Li, Han-Xiong Wu, Huai-Ning Univ Jinan Sch Elect Engn Jinan 250022 Shandong Peoples R China Cent South Univ State Key Lab High Performance Complex Mfg Changsha Hunan Peoples R China City Univ Hong Kong Dept Syst Engn & Engn Management Hong Kong Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Sci & Technol Aircraft Control Lab Beijing 100191 Peoples R China

In this paper, the exponential stabilization problem is addressed for a class of nonlinear parabolic partial differential equation (PDE) systems via sampled-data fuzzy control approach. Initially, the nonlinear PDE system is accurately represented by the Takagi-Sugeno (T-S) fuzzy PDE model. Then, based on the T-S fuzzy PDE model, a novel time-dependent Lyapunov functional is used to design a sampled-data fuzzy controller under spatially point measurements such that the closed-loop fuzzy PDE system is exponentially stable with a given decay rate. The stabilization conditions are presented in terms of a set of linear matrix inequalities (LMIs). Finally, simulation results on the control of the diffusion equation and the FitzHugh-Nagumo (FHN) equation to illustrate the effectiveness of the proposed design method. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Exponential stability Sampled-data control Fuzzy control distributed parameter systems Linear matrix inequality (LMI)

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Sliding mode boundary control of linear parabolic distributed parameter systems with space-dependent parameters

Sliding mode boundary control of linear parabolic distribute...

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Chinese Automation Congress (CAC)

作者： Chunxia Bao Baotong Cui Wei Wu School of IoT Engineering Jiangnan University Wuxi China

ISBN: (纸本)9781665426480

This paper studies the sliding mode boundary control of parabolic distributed parameter systems with space-dependent parameters. A sliding surface is selected to guarantee that the system is asymptotically stable. The Lyapunov method is used to design a sliding mode boundary controller to guarantee that the trajectories of the system subject to disturbances reach the sliding surface within the prescribed time. Some numerical simulations are finally provided to illustrate the effectiveness of the proposed control method.

关键词： Asymptotic stability Upper bound Automation Control systems Numerical simulation Trajectory distributed parameter systems

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Iterative Learning Control for Discrete distributed parameter systems With Randomly Varying Trial Lengths

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IEEE ACCESS 2019年 7卷 115583-115593页

作者： Zhang, Weijie Dai, Xisheng Tian, Senping Guangxi Univ Sci & Technol Sch Elect & Informat Engn Liuzhou 545006 Peoples R China South China Univ Technol Sch Automat Sci & Engn Guangzhou 510640 Guangdong Peoples R China

In this paper, iterative learning control (ILC) is employed in discrete spatial-temporal parabolic distributed parameter systems (DPSs), where the trial lengths vary randomly. A distributed ILC strategy is proposed, in which containing spatial variable, utilizes all past tracking information to improve current performance. Through rigorous theoretical analysis, the convergence of the system output error is proved under mathematical expectation along the iteration axis. Finally, the proposed method is applied to numerical simulation to illustrate its effectiveness.

关键词： Iterative learning control distributed parameter systems partial difference equations random convergence

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Tracking model predictive control and moving horizon estimation design of distributed parameter pipeline systems

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COMPUTERS & CHEMICAL ENGINEERING 2023年第1期178卷

作者： Zhang, Lu Xie, Junyao Dubljevic, Stevan Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 2V4 Canada

This manuscript proposes moving horizon control and state/parameter estimation designs for pipeline networks modelled by partial differential equations (PDEs) with boundary actuation. The spatial-temporal pressure and velocity dynamics within the pipelines are described by a system of six coupled one-dimensional first-order nonlinear hyperbolic PDEs. To address the discrete-time modelling challenge and preserve the infinite-dimensional nature of the pipeline system, the Cayley-Tustin transformation is deployed for model time discretization without any spatial discretization or model reduction. Considering the lack of full state information across the entire pipeline manifold, unknown states and uncertain parameters are estimated using moving horizon estimation (MHE). Based on the estimated states and parameters, a tracking model predictive control (MPC) strategy for the discrete-time infinite-dimensional pipeline system is proposed, which enables specific operation while ensuring physical constraint satisfaction. The effectiveness of the proposed controller and estimator designs is demonstrated via numerical examples.

关键词： distributed parameter systems Hyperbolic partial differential equations Model predictive control Moving horizon estimation Pipelines

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