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 ...
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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.
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, ...
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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.
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...
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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.
作者:
A. HastirJ.J. WinkinD. DochainUniversity 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 distributedparameter, i.e. infinite-dimensional state space, systems is considered. A weakened concept of Fréchet differentiability (( Y,X )-Fréchet different...
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Local exponential (exp.) stability of nonlinear distributedparameter, 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.
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 al...
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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 distributedparameter 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.
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 ine...
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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.
作者:
Wang, Zi-PengLi, Han-XiongWu, Huai-NingUniv 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 sy...
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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.
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...
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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.
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, i...
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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.
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...
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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.
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