This paper considers the spatial penalization of the pairwise state estimate differences as used to enforce consensus in spatially distributed filters. A spatially distributed process, described by a parabolic partial...
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This paper considers the spatial penalization of the pairwise state estimate differences as used to enforce consensus in spatially distributed filters. A spatially distributed process, described by a parabolic partial differential equation is assumed to have a network of in-domain sensors. Each spatially distributed filter corresponds to a single sensor in the network and the goal is for these filters to collaboratively reach a consensus on the process state estimate. To enhance the agreement of the spatially distributed filters, the spatial gradient of the pairwise difference of state estimates is used as a means to penalize their disagreement. Additionally, a proportional and an integral penalization of the pairwise differences are also examined in order to produce a spatial proportional-integral-derivative penalization. To address the partial connectivity, certain conditions on the communication topology are given implicitly in terms of the inner product of the state estimation errors and their pairwise differences. Simulation studies provide an insight on the effects of this spatial penalizations. (C) 2013 Elsevier B.V. All rights reserved.
The purpose of this two-part work is to apply active mode localization techniques to distributed parameter systems where control ncfuntor and sensor placement is a limiting factor In this paper;Part 2 of the study, th...
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The purpose of this two-part work is to apply active mode localization techniques to distributed parameter systems where control ncfuntor and sensor placement is a limiting factor In this paper;Part 2 of the study, the SVD eigenvector shaping technique examined in Part 1 is utilized to numerically and experimentally localize the response of a simply supported beam. This is done for two reasons. First, it demonstrates the application of this modified mode localization technique to a distributedparameter system. Second, it shows that it is possible to use this method to produce vibration isolation, reducing the absolute displacements in designated portions of the system while simultaneously curtailing the number of necessary control sensors and actuators.
A Nyquist graphical stability criterion is developed for distributedparameter, possible unstable, single-loop systems. Practical conditions are presented for existence, uniqueness, causality, and asymptotic or expone...
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A Nyquist graphical stability criterion is developed for distributedparameter, possible unstable, single-loop systems. Practical conditions are presented for existence, uniqueness, causality, and asymptotic or exponential stability of the closed-loop impulse response. The hypotheses are given for the transfer function only and do not require any knowledge of its time-domain impulse response.< >
The previously developed technology of multi-objective optimization of deterministic systems with distributed parabolic parameters can be applied in typical situations with interval uncertainties of parametric charact...
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The previously developed technology of multi-objective optimization of deterministic systems with distributed parabolic parameters can be applied in typical situations with interval uncertainties of parametric characteristics of controlled plants. Minimax problems of mathematical programming are formed in accordance with a control strategy based on the principle of the best guaranteed result of a expanded set of arguments, which includes all permissible realizations of uncertain factors, in the case of estimating a given accuracy of approximation to a required finite system state in uniform metric. These problems are solved by an alternance method that uses the Chebyshev properties of desired extermals and fundamental laws of the problem domain. This paper also presents an example of multi-objective optimization of temperature modes of induction heating of metallic semifinished products before plastic deformation operations in technological complexes for pressure treatment of metal, which is of independent interest.
The paper deals with the distributed parameter systems described by coupled partial differential equations with singular matrix coefficients. Initial-boundary-value problems are considered in the light of both singula...
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The paper deals with the distributed parameter systems described by coupled partial differential equations with singular matrix coefficients. Initial-boundary-value problems are considered in the light of both singular 1d systems theory and the Fourier approach to distributed parameter systems. The method presented in this paper gives the possibility of determining acceptable initial-boundary conditions. An illustrative example is given.
In the modeling of distributed parameter systems, it is sometimes necessary to utilize system observations to identify or estimate the boundary conditions of the field. For example, in dealing with water resources sys...
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In the modeling of distributed parameter systems, it is sometimes necessary to utilize system observations to identify or estimate the boundary conditions of the field. For example, in dealing with water resources systems, it may be desired to identify and characterize sources of water pollution on the basis of measure ments of pollution concentrations downstream from suspected sources. A computational technique based on pattern recogni tion has been found to be relatively effective in this application. However, the algorithms involved are highly computation- intensive and therefore tend to place a considerable burden on most conventional digital computer facilities. Array processors, though primarily designed for signal processing are playing an increasingly important role in the modeling and simulation of physical systems. It is a purpose of this paper to describe the ap plication of a relatively small and inexpensive peripheral array processor to this problem and to evaluate its performance.
In this paper, a data-driven iterative learning control with neural network-based optimisation method for distributed parameter systems is presented to solve a class of problems caused by the imprecise mathematical mo...
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In this paper, a data-driven iterative learning control with neural network-based optimisation method for distributed parameter systems is presented to solve a class of problems caused by the imprecise mathematical model. The forward difference format is used to establish a linear relationship between input and output data, which is the only information available. However, this also leads to an unknown parameter matrix of the system. To overcome this problem, the radial basis function neural network is used to form a mapping relation from the desired output to the desired input, and the iterative learning algorithm of neural network weight is obtained by optimising the system performance indexes. Then, a detailed theoretical analysis based on composite energy function is given. Moreover, unlike traditional iterative learning control task tracking the whole trajectory, tracking time terminal is taken into account in this paper. Finally, simulation results show the feasibility of the theory.
This paper presents a new model-based fault resilient control scheme for a class of nonlinear distributed parameter systems (DPS) represented by parabolic partial differential equations (PDE) in the presence of actuat...
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This paper presents a new model-based fault resilient control scheme for a class of nonlinear distributed parameter systems (DPS) represented by parabolic partial differential equations (PDE) in the presence of actuator faults. A Luenberger-like observer on the basis of nonlinear PDE representation of DPS is developed with boundary measurements. A detection residual is generated by taking the difference between the measured output of the DPS and the estimated one given by the observer. Once a fault is detected, an unknown actuator fault parameter vector together with a known basis function is utilized to adaptively estimate the fault dynamics. A novel tuning algorithm is derived to estimate the unknown actuator fault parameter vector. Next, in order to achieve resilience, the controller from the healthy scenario is adjusted to mitigate the faults by using both estimated fault dynamics and a secondary measurement. Subsequently, an explicit formula is developed to estimate the time-to-resilience (TTR). Finally, a nonlinear example is utilized to illustrate the effectiveness of the proposed scheme.
In this work dissipativity of linear neutral distributed parameter systems has been addressed. Delay-dependent sufficient conditions for the dissipativity with respect to the infinite-dimensional version of energy sup...
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In this work dissipativity of linear neutral distributed parameter systems has been addressed. Delay-dependent sufficient conditions for the dissipativity with respect to the infinite-dimensional version of energy supply rate (Q(1), S(1), R(1)) characterized exclusively by unbounded operator Q(1) are established in terms of linear operator inequalities (LOIs). Finally, the 3-dimensional heat equation illustrates our result. (C) 2011 Elsevier Ltd. All rights reserved.
We propose a method for synthesizing energy-optimal controllers in open and closed domains of space-time control actions for linear multidimensional objects with distributed parabolic parameters under the conditions o...
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We propose a method for synthesizing energy-optimal controllers in open and closed domains of space-time control actions for linear multidimensional objects with distributed parabolic parameters under the conditions of a given accuracy of uniform approximation of the final state of the system to the desired spatial distribution of the controlled variable. The approach is based on the previously developed alternance method for constructing parameterizable algorithms of optimal program control, which uses the fundamental laws of the subject area. It is shown that the desired equations of the controllers are reduced to linear feedback laws with restrictions on incomplete observation of the state of the object with nonstationary transmission coefficients, which are determined by preliminary calculation of the program control action.
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