This paper proposes an optimal way of allocating collocated input-output pairs for stabilizing distributed parameter systems. We first introduce a finite-dimensional reduced model from sampled initial responses of the...
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This paper proposes an optimal way of allocating collocated input-output pairs for stabilizing distributed parameter systems. We first introduce a finite-dimensional reduced model from sampled initial responses of the systems via the POD (Proper Orthogonal Decomposition)Galerkin method. Next, optimal gains of the stabilizing controller for the reduced systems are designed by the stable manifold method that is an exact numerical solver of Hamilton-Jacobi equations. Filially, we present three allocation methods derived front. state shape matching, dissipation enhancement, and their mixed evaluation, and we show that the optimal allocations car be associated with energy controls in terms of port representations. (C) 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
One of the important problem related to the usage of wireless sensor networks in harsh environments is the identification of the states of the physical variables in the field, based on the measurements provided by the...
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ISBN:
(纸本)9789898111623
One of the important problem related to the usage of wireless sensor networks in harsh environments is the identification of the states of the physical variables in the field, based on the measurements provided by the sensors. The sensor networks allow the usage of the multivariable estimation techniques in distributed parameter systems. The paper presents an application of a multivariable auto-regression estimation technique for identification in distributed parameter systems, based on a sensor network. A case study was presented for identification in a heat diffusion process.
In this paper, approximation of the spatio-temporal response of a hyperbolic distributedparameter system with the use of the proper orthogonal decomposition method is discussed. Based on a simulation data set, repres...
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ISBN:
(纸本)9781457709142
In this paper, approximation of the spatio-temporal response of a hyperbolic distributedparameter system with the use of the proper orthogonal decomposition method is discussed. Based on a simulation data set, representing the profile of a selected process variable, the model reduction procedure is performed. The procedure consists in the projection of the original data into the subspace represented by eigenvectors of the spatial covariance matrix, corresponding to its highest eigenvalues. Influence of the approximation order on the response approximation error and on the data compression ratio is also analyzed.
Numerical results from an investigation of the Optimal control of a parabolic distributedparameter system are presented. The results from a matrix Riccati formulation are contrasted with those using a variable metric...
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Numerical results from an investigation of the Optimal control of a parabolic distributedparameter system are presented. The results from a matrix Riccati formulation are contrasted with those using a variable metric method. Spurious oscillations in the optimal control given by the Riccati algorithm are shown to stem from the use of Simpson's rule to approximate the integrals. It is concluded that the trapezoidal rule is to be preferred.
Bifurcation theory provides a powerful tool for analyzing the nonlinear dynamic behavior of process systems. However, although the theory in principle applies to lumped as well as distributedparameter processes, it i...
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Bifurcation theory provides a powerful tool for analyzing the nonlinear dynamic behavior of process systems. However, although the theory in principle applies to lumped as well as distributedparameter processes, it is in practice necessary to reduce the order of distributed (partial differential equations, PDE) models prior to application of the theory. As shown in this paper, simply applying some ad hoc discretization method such as finite differences or finite elements, can result in spurious bifurcations and erroneous predictions of stability. To enable detection of such anomalities, and to aid in the selection of a proper model order, we propose a method for estimating the error introduced by the model reduction. Apart from simply providing a label of confidence in the results of the bifurcation analysis, the estimated error can be used to improve the quality of the reduced order model. For this purpose we propose a method based on dynamically moving the discretization mesh such as to minimize the discretization error. The proposed method is based on principles from feedback control, and is both very simple and highly robust compared with existing so-called moving mesh methods. As an application we consider bifurcation analysis of a heat-integrated fixed-bed reactor. (C) 2003 Elsevier Ltd. All rights reserved.
Thanks to development of information technology, the so-called virtual software environments offer wide possibilities for the estimation of time-space dynamical characteristics of energy systems, including modeling, c...
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ISBN:
(纸本)9781467320665
Thanks to development of information technology, the so-called virtual software environments offer wide possibilities for the estimation of time-space dynamical characteristics of energy systems, including modeling, control and design of distributed parameter systems. Based on these advances, we present a novel approach to control energy systems as lumped-input and distributed-parameter-output systems. An adaptive-predictive controller is deployed to control temperature fields to ensure optimal conditions for the desulphurization process in a coal-burning fluidized bed furnace, demonstrating the potential of the proposed methodology.
作者：
Feng, YunWang, YaonanZhang, YazhiHunan Univ
Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China Hunan Univ
Natl Engn Res Ctr Robot Visual Percept & Control Changsha 410082 Hunan Peoples R China Hunan Inst Engn
Coll Elect & Informat Engn Xiangtan 411104 Peoples R China
Different from the traditional model-based fault diagnosis paradigm which is established upon the well-known observer design and analysis, a novel data-driven framework is proposed by combing systems modeling with fau...
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Different from the traditional model-based fault diagnosis paradigm which is established upon the well-known observer design and analysis, a novel data-driven framework is proposed by combing systems modeling with fault detection for a class of 1-D unknown distributed parameter systems. The key idea is to transfer the on-line modeling error into the residual signal for fault detection. The proposed methodology only utilizes the I/O data and does not require extra knowledge of the system model, which increases its usability at large. Numerical simulations on a commonly used benchmark are presented for method validation. Copyright (C) 2022 The Authors.
This work examines the effects of actuator location in networked distributed parameter systems. In particular, the effects of the optimal actuator location for one networked system can impact the collective behavior o...
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ISBN:
(纸本)9781479986842
This work examines the effects of actuator location in networked distributed parameter systems. In particular, the effects of the optimal actuator location for one networked system can impact the collective behavior of the networked systems. The actuator optimization cannot be performed on the individual networked systems, but must be considered for the aggregate of the networked systems. To tackle the actuator optimization problem, the networked systems are viewed collectively and with the implementation of a synchronization controller, the resulting closed-loop aggregate system is parameterized by the actuator location. Using different metrics for the success of synchronization, the resulting actuator optimization problem is recast as a minimization of a performance index. The solution to this optimization is expressed as the minimization of the trace of a location-parameterized Lyapunov operator. Simulation studies on seven networked diffusion partial differential equations in one spatial dimension reveal the effects of a correct actuator placement on synchronization.
Closing the loop around an exponentially stable single-input single-output regular linear system, subject to input relay hysteresis and compensated by an integral controller, is shown to ensure asymptotic tracking of ...
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ISBN:
(纸本)085296708X
Closing the loop around an exponentially stable single-input single-output regular linear system, subject to input relay hysteresis and compensated by an integral controller, is shown to ensure asymptotic tracking of constant reference signals, provided that (a) the steady-state gain of the linear part of the plant is positive, (b) the positive integrator gain is sufficiently small and (c) the reference value is feasible in a very natural sense.
This paper considers the iterative learning control (ILC) problem for a class of uncertain linear distributed parameter systems with time delay. A P-type ILC scheme is presented for distributed parameter systems. Then...
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ISBN:
(纸本)9781728105215
This paper considers the iterative learning control (ILC) problem for a class of uncertain linear distributed parameter systems with time delay. A P-type ILC scheme is presented for distributed parameter systems. Then, using a Lyapunov-like approach, we derive sufficient conditions for tracking error convergence in the sense of L-2 norm in terms of linear matrix inequalities based on rigorous analysis, which can also guarantee the monotonic convergence of the input error in some given norm. The results of numerical simulations are presented to illustrate the effectiveness of the proposed ILC approach.
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