This paper investigates robust quantized output feedback control of nonlinear Markovian jump distributed parameter systems (MJDPSs) with incomplete transition rates. Considering the digital communication channel in pr...
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This paper investigates robust quantized output feedback control of nonlinear Markovian jump distributed parameter systems (MJDPSs) with incomplete transition rates. Considering the digital communication channel in practical applications, the data of measured output and control input is quantized before transmission, by mode-dependent quantizer. Furthermore, a randomly occurring communication fault phenomenon is noticed in stability analysis, and is described by Bernoulli distributed white sequences. Based on Takagi-Sugeno (T-S) fuzzy model and dynamic parallel distributed compensate principle, a novel output feedback controller is developed. The conditions, to ensure that the MJDPSs are stochastically stable with mixed L-2-L-infinity/H-infinity performance, are given in terms of linear matrix inequalities (LMIs), and controller gains can be obtained by LMI toolbox. Finally, an example is provided to illustrate the effectiveness of the proposed method. (C) 2019 Elsevier Inc. All rights reserved.
Optimal location of point sensors and controllers in stochastic regulator problems for evolution equations is considered using a semigroup approach and the separation principle. The existence of an optimal location is...
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Optimal location of point sensors and controllers in stochastic regulator problems for evolution equations is considered using a semigroup approach and the separation principle. The existence of an optimal location is established under very general hypotheses. The theory is illustrated by specializing to a diffusion equation and some computational results are presented.
The robust control problem for a class of mismatched uncertain parabolic distributed parameter systems is studied under conditions of the uncertain terms being norm-bounded. The proposed robust controller can guarante...
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The robust control problem for a class of mismatched uncertain parabolic distributed parameter systems is studied under conditions of the uncertain terms being norm-bounded. The proposed robust controller can guarantee the system states to be the uniform ultimate boundedness, and their bound can be arbitrarily small. And, the proposed control method is successfully applied to a typical diffusion-reaction process with uncertain terms.
Optimal filtering and smoothing algorithms for linear discrete-time distributed parameter systems are derived by a unified approach based on the Wiener-Hopf theory. The Wiener-Hopf equation for the estimation problems...
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Optimal filtering and smoothing algorithms for linear discrete-time distributed parameter systems are derived by a unified approach based on the Wiener-Hopf theory. The Wiener-Hopf equation for the estimation problems is derived using the least-squares estimation error criterion. Using the basic equation, three types of the optimal smoothing estimators are derived, namely, fixed-point, fixed-interval, and fixed-lag smoothers. Finally, the results obtained are applied to estimation of atmospheric sulfur dioxide concentrations in the Tokushima prefecture of Japan.
Necessary and sufficient conditions for the approximate controllability and approximate observability of a singular distributedparameter system are obtained in the sense of distributional solution. These general resu...
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Necessary and sufficient conditions for the approximate controllability and approximate observability of a singular distributedparameter system are obtained in the sense of distributional solution. These general results are used to examine the approximate controllability and approximate observability of the Dzektser equation in the Theory of Seepage.
This note presents an indirect method for computing coprime factors for a class of linear time-invariant distributed parameter systems. The method overcomes the numerical instability of an existing direct decompositio...
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This note presents an indirect method for computing coprime factors for a class of linear time-invariant distributed parameter systems. The method overcomes the numerical instability of an existing direct decomposition method. It is shown that coprime factorization can be reduced to constructing simultaneously stabilizing finite dimensional controllers, for which an efficient iterative procedure is developed.
This article considers sliding mode control of a class of parabolic linear uncertain distributed parameter systems with time-varying delays. The sliding mode controller design does not contain time delay terms and dri...
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This article considers sliding mode control of a class of parabolic linear uncertain distributed parameter systems with time-varying delays. The sliding mode controller design does not contain time delay terms and drives the state trajectory of the system to the sliding manifold in finite time. A sufficient condition of asymptotic stability for the sliding motion is derived. A simulation example is presented to illustrate effectiveness of the proposed method.
We consider the problem of optimally designing sensors for observation of a class of distributed parameter systems. The design of sensors concerns the choice of measurement conditions so that the information provided ...
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We consider the problem of optimally designing sensors for observation of a class of distributed parameter systems. The design of sensors concerns the choice of measurement conditions so that the information provided by measurements is maximal. This problem has been posed as a deterministic optimal control problem for a system equation of the Riccati type which governs a filter covariance. In the present study we introduce a functional called a sensitivity criterion by extending the Fisher information matrix to function spaces. It is shown that maximizing this criterion leads to a suboptimal solution of the sensor design problem associated with an infinite-dimensional state estimation problem. The existence theorem for a type of measurement control problem is proved and some numerical results are presented.
An iterative learning control problem for a class of uncertain linear parabolic distributed parameter systems is discussed,which covers many processes such as heat and mass transfer,convection diffusion and *** condit...
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An iterative learning control problem for a class of uncertain linear parabolic distributed parameter systems is discussed,which covers many processes such as heat and mass transfer,convection diffusion and *** condition of allowing system state initially to have error in the iterative process a closed-loop P-type iterative learning algorithm is presented,and the sufficient condition of tracking error convergence in L2 norm is ***,the convergence of the tracking error in L2 and W1,2 space is proved by using Gronwall-Bellman inequality and Sobolev *** the end,a numerical example is given to illustrate the effectiveness of the proposed method.
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.
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