Identification of spatially varying parameters in distributed parameter systems from noisy data is an ill-posed problem. The concept of regularization, widely used in solving linear Fredholm integral equations, is dev...
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Identification of spatially varying parameters in distributed parameter systems from noisy data is an ill-posed problem. The concept of regularization, widely used in solving linear Fredholm integral equations, is developed for the identification of parameters in distributed parameter systems. A general regularization identification theory is first presented and then applied to the identification of parabolic systems. The performance of the regularization identification method is evaluated by numerical experiments on the identification of a spatially varying diffusivity in the diffusion equation.
This work is concerned with the design and effects of the synchronization gains on the synchronization problem for a class of networked distributed parameter systems. The networked systems, assumed to be described by ...
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This work is concerned with the design and effects of the synchronization gains on the synchronization problem for a class of networked distributed parameter systems. The networked systems, assumed to be described by the same evolution equation in a Hilbert space, differ in their initial conditions. To address the synchronization problem, a coupling term containing the pairwise state differences of all the networked systems weighted by the synchronization gains is included in the controller of each networked system. By considering the aggregate closed-loop systems, an optimization scheme for the synchronization gains is proposed by minimizing an appropriate measure of synchronization. The integrated control and synchronization design is subsequently cast as an optimal control problem, the solution of which is found via the solution of parameterized operator Lyapunov equations. An alternative to the optimization of the synchronization gains is also proposed in which the adaptation of synchronization gains is derived from Lyapunov redesign methods. Both choices of the proposed synchronization controllers aim at achieving both the control and the synchronization objectives. An extensive numerical study examines the various aspects of the optimization and adaptation of the gains on the control and synchronization of networked 1D parabolic differential equations.
In this paper, a fairly complete parallel of the finite-dimensional root locus theory is presented for quite general, nonconstant coefficient, even order ordinary differential operators on a finite interval with contr...
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In this paper, a fairly complete parallel of the finite-dimensional root locus theory is presented for quite general, nonconstant coefficient, even order ordinary differential operators on a finite interval with control and output boundary conditions representative of a choice of collocated point actuators and sensors. Root-locus design methods for linear distributed parameter systems have also been studied for some time and the primary difficulties in rigorously interpreting root-locus conclusions for distributed parameter systems are well known. First, the transfer function of a distributedparameter system may not be meromorphic at infinity so that many of the standard Rouche arguments, required even in the lumped case to determine the asymptotic behavior of the root loci, are not generally valid. Another difficulty is that the infinitesimal generator in the state-space model for a closed-loop system may not be selfadjoint, accretive or even satisfy the spectrum determined growth condition. Thus, regardless of whether the root loci-interpreted as closed-loop eigenvalues-lie in the open left half-plane, additional analysis would be required to conclude that the closed-loop system would be asymptotically stable. Formulating the systems in the classical format of a boundary control problem, the asymptotic analysis of the root loci can be based on the pioneering work by Birkhoff on eigenfunction expansions for boundary value problems, work that predated and indeed motivated the development of spectral theory in Hilbert space. Birkhoff's work also contains an asymptotic expansion of eigenfunctions in the spatial variable, generalizing the earlier Sturm-Liouville theory for second-order operators. By further extending this general asymptotic analysis to also include expansions in the gain parameter, a rigorous treatment of the open- and closed-loop transfer functions and of the corresponding return difference equation can be presented. The asymptotic analysis of the re
In this paper, iterative learning control(ILC) technique is applied to a class of discrete parabolic distributed parameter systems described by partial difference equations. A P-type learning control law is establishe...
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In this paper, iterative learning control(ILC) technique is applied to a class of discrete parabolic distributed parameter systems described by partial difference equations. A P-type learning control law is established for the system. The ILC of discrete parabolic distributed parameter systems is more complex as 3D dynamics in the time, spatial and iterative domains are *** overcome this difficulty, discrete Green formula and analogues discrete Gronwall inequality as well as some other basic analytic techniques are utilized. With rigorous analysis, the proposed intelligent control scheme guarantees the convergence of the tracking error. A numerical example is given to illustrate the effectiveness of the proposed method.
The output feedback control problem for a class of nonlinear distributed parameter systems with limited number of continuous measurement sensors that describes a wide range of physico-chemical systems is investigated ...
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The output feedback control problem for a class of nonlinear distributed parameter systems with limited number of continuous measurement sensors that describes a wide range of physico-chemical systems is investigated using adaptive proper orthogonal decomposition (APOD) method. Specifically, APOD is used to initiate and recursively revise locally valid reduced order models (ROMs) that approximate the dominant dynamic behavior of such physico-chemical systems. The controller is designed based on ROMs by combining a robust state controller with an APOD-based nonlinear Luenberger-type switching dynamic observer of the system states to reduce measurement sensor requirements. The important static observer requirements on the number of measurement sensors (that they must be supernumerary to the ROM dimension) and their location are circumvented by synthesizing dynamic observers. Three different approaches are introduced to recursively compute the dynamic observer gains at the ROM revisions. The stability of the closed-loop system is proven via Lyapunov and hybrid system stability arguments without invoking the separation principle between control and observation. The proposed method is successfully used to regulate a physico-chemical system that can be described in the form of the Kuramoto-Sivashinsky equation when the process exhibits significant nonlinear behavior. (C) 2015 Elsevier Ltd. All rights reserved.
This paper presents control laws for distributed parameter systems of parabolic and hyperbolic types with unknown spatially varying parameters. These laws, based on the model reference adaptive control approach, guara...
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This paper presents control laws for distributed parameter systems of parabolic and hyperbolic types with unknown spatially varying parameters. These laws, based on the model reference adaptive control approach, guarantee asymptotic tracking of the output of the reference model by the output of the plant for arbitrary time invariant, but spatially varying reference input. The novel capabilities of the algorithms proposed are providing reduced sensitivity to measurement noise due to the reduced order of the spatial differentiation of the output data and permitting on-line estimation of the spatially varying plant parameters, constructively enforceable through the reference input and/or boundary conditions. The parameter estimation is carried out by means of an auxiliary system with the time-varying parameters that simultaneously converge in L-2 to plant parameters when appropriate input signals in the reference model are used. The orthogonal expansions of these time-varying parameters, which can be computed by passing the auxiliary system parameters through the integrator block, converge to the plant parameters pointwise if the latter are sufficiently smooth. The parameter convergence is obtained by combining the adaptation laws with sufficiently rich input signals, referred to as generators of persistent excitation, which guarantee the existence of a unique steady state for the parameter errors. Copyright (C) 2001 John Wiley & Sons, Ltd.
In this paper, the iterative learning control (ILC) problem is investigated for a class of time-invariant parabolic singular distributed parameter systems. Initially, the singular distributed parameter systems is deco...
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In this paper, the iterative learning control (ILC) problem is investigated for a class of time-invariant parabolic singular distributed parameter systems. Initially, the singular distributed parameter systems is decomposed into infinite number of singular systems based on the separation principle. Meanwhile, the slow-fast subsystems are introduced via singular value decomposition method. Then, a novel mixed PD-type ILC algorithm with finite dimension is designed for the low dimensional slow part and the corresponding convergence conditions are manifested. With the proposed controller, the output error of high dimensional fast complement can satisfy the given value instead of neglecting the effect of high dimensional modes. Furthermore, under the aforesaid ILC law and the appropriate number of the low dimensional slow part, the resulting tracking error of parabolic singular distributed parameter systems can converge to any small tracking accuracy. Finally, simulation results on the distributed building automatic temperature system verify the convergence and effectiveness of the mixed PD-type ILC algorithm.
This paper deals with the iterative learning control issue for multi-input multi-output singular distributed parameter systems (SDPSs) with parabolic and hyperbolic type, which described by coupled partial differentia...
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This paper deals with the iterative learning control issue for multi-input multi-output singular distributed parameter systems (SDPSs) with parabolic and hyperbolic type, which described by coupled partial differential equations with singular matrix coefficients. Initially, applying the singular value decomposition theory to SDPSs, an equivalent dynamic decomposition form is derived. Then, the estimation of the relationship between the learning system substates and output tracking error are constructed in the light of P-type update learning scheme under some assumptions. Moreover, two sufficient conditions are presented to ensure that the tracking error is convergent in the sense of L-2 norm by employing the contracting mapping principle as well as some basic differential inequalities. Finally, two numerical examples are shown to demonstrate the validity of the developed theoretical results.
The spectral factorization problem of a scalar coercive spectral density is considered in the framework of the Callier-Desoer algebra of distributedparameter system transfer functions. Criteria are given for the infi...
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The spectral factorization problem of a scalar coercive spectral density is considered in the framework of the Callier-Desoer algebra of distributedparameter system transfer functions. Criteria are given for the infinite product representation of a meromorphic coercive spectral density of finite order and for the convergence of infinite product representations of spectral factors, i.e., for the convergence of the symmetric extraction method for solving the spectral factorization problem of such spectral density. These convergence criteria are applied to the solution of the linear-quadratic optimal control problem by spectral factorization for a specific class of semigroup Hilbert state-space systems with a Riesz-spectral generator. The speed of convergence of the symmetric extraction method is also considered. As an example a damped vibrating string model is handled.
The operational matrix of integration and a one-shot operational matrix for repeated integration (OSOMRI) are used to estimate the parameters and the initial and boundary conditions of linear, time-invariant, lumped-p...
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The operational matrix of integration and a one-shot operational matrix for repeated integration (OSOMRI) are used to estimate the parameters and the initial and boundary conditions of linear, time-invariant, lumped-parametersystems. It is demonstrated that OSOMRI provides better accuracy than the conventional operational matrix of integration. An algorithm for distributed-parameter system identification using Fourier series is included. A comparative study of the estimates obtained by the proposed method for both types of system with those in the literature obtained by other methods is carried out.
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