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4 edition of Distributed parameter systems: Modelling and identification found in the catalog.

Distributed parameter systems: Modelling and identification

proceedings of the IFIP Working Conference, Rome Italy, June 21-24, 1976 (Lecture notes in control and information sciences)

by

  • 159 Want to read
  • 23 Currently reading

Published by Springer-Verlag .
Written in English


The Physical Object
Number of Pages458
ID Numbers
Open LibraryOL7442641M
ISBN 100387084053
ISBN 109780387084053

Relevant topics for the symposium program include: Identification of linear and multivariable systems, identification of nonlinear systems, including neural networks, identification of hybrid and distributed systems, Identification for control, experimental modelling in process control, vibration and modal analysis, model validation, monitoring. The simplified model is applied to the FRA simulation of a transformer winding. In order to identify the distributed parameters of the model, an intelligent learning technique, rooted from particle swarm optimiser with passive congregation (PSOPC) is utilised. Simulations and discussions are presented to explore the proposed optimization by: 4. Book Title:Simulation Modelling and Analysis This seniorgraduatelevel text is the classic text in its field and established itself as the authoritative source on the theory and practice of . M. Cantoni and C.-Y. Kao, "Stability analysis for distributed-parameter systems interconnected via feedback channels with time-varying delay." In Proceedings of the 51st IEEE Conference on Decision and Control, Hawaii, M.P. Kearney and M. Cantoni, "MPC-based reference management for automated irrigation channels.".


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Distributed parameter systems: Modelling and identification Download PDF EPUB FB2

Distributed Parameter Systems: Modelling and Identification Proceedings of the IFIP Working Conference, Rome, Italy, June 21–24, Editors: Ruberti, A. (Ed. Distributed Parameter Systems: Modelling and Identification Proceedings of the IFIP Working Conference Rome, Italy, June 21–24, Optimal Sensor Networks Scheduling in Identification of Distributed Parameter Systems (Lecture Notes in Control and Information Sciences) [Maciej Patan] on *FREE* shipping on qualifying offers.

Here is a coherent approach to sensor network scheduling for parameter estimation in dynamic distributed systems modeled with partial differential equationsCited by: Get this from a library.

Distributed parameter systems: modelling and identification: proceedings of the IFIP Working Conference, Rome Italy, June[Antonio Ruberti; International Federation for Information Processing.;].

Pritchard A.J., Ryan E.P. () Control and identification of distributed parameter systems. In: Ruberti A. (eds) Distributed Parameter Systems: Modelling and Identification.

Lecture Notes in Control and Information Sciences, vol by: 4. An abstract approximation theory for the identification of linear degenerate distributed parameter systems is Distributed parameter systems: Modelling and identification book.

Central to the approach is an abstract approximation result for regular and degenerate implicit distributed systems in the spirit of the Trotter-Kato Theorem for the approximation of linear semigroups. ISBN: OCLC Distributed parameter systems: Modelling and identification book Description: 1 online resource (v, pages) Contents: Identification of distributed parameter systems: Non-computational aspects --Some aspects of modelling problems in distributed parameter systems --Numerical implementation of distributed parameter filters with application to problems in air pollution --On.

Vp 1 dx The superscrit " y indicates derivative Distributed Parameter Identification in Drying Equations /Y The superscrit * ve / u. 99 indicates derivati Boundary condition; p n | = 0 1 P on. = r x J O, T £ Final conditions: p. (x,T) = 0 and p2(T) = 0 From Lagrangian formulation we obtain the gradient: real case of drying by: 1.

Much of the current emphasis on identification problems for distributed parameter systems is on the computational aspects of solving equations and least-squares optimization. Sensor networks have recently come into prominence because they hold the potential to revolutionize a wide spectrum of both civilian and military applications.

An ingenious characteristic of sensor networks is the distributed nature of data. 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 by: An exploration of physical modelling and experimental issues that considers identification of structured models such as continuous-time linear systems, multidimensional systems and nonlinear systems.

It gives a broad perspective on modelling, identification and its applications. Linear smoothing in Hilbert space. Distributed Parameter Systems: Modelling and Identification, pp (System Identification) in the Hilbert space setting. The basic theoretical Author: Arunabha Bagchi.

T1 - Compositional modelling of distributed-parameter systems. AU - Maschke, B.M. AU - van der Schaft, Arjan. PY - Y1 - N2 - The Hamiltonian formulation of distributed-parameter systems has been a challenging reserach area for quite some by: Author: Gilles, Ernst Dieter; Genre: Book Chapter; Published in Print: ; Title: Modelling and simulation of distributed parameter systemsCited by: 3.

For the MATLAB & Simulink based software support of modelling, control and design of Distributed Parameter Systems given on complex 3D domains of definition, the programming environment Distributed Parameter Systems Blockset for MATLAB & Simulink (DPS Blockset) as Third-Party MathWorks Product has been developed by the Institute of Automation Cited by: 2.

The large scale distributed parameter systems modelling, control and operational maximization have been an area of interest to both academic and industrial researchers. Bridging the time and length scales in these systems, from the standpoint of modelling and parameter identification that serve the purpose of prediction and control is our main research focus.

Alper Erturk, Virginia Tech, USA, is a Graduate Research Assistant in the Center for Intelligent Material Systems and Structures at Virginia has written 1 book chapter and over 30 articles in various international journals and conference proceedings.

His recent article on distributed-parameter electromechanical modelling of piezoelectric energy harvesters. Modelling and Systems Parameter Estimation for Dynamic Systems presents a detailed examination of the estimation techniques and modeling problems. The theory is furnished with several illustrations and computer programs to promote better understanding of system modeling and parameter estimation.

The material is presented in a way that makes for easy reading and Cited by: In this paper, we studied the identification issue of one class of distributed parameter systems based on the Chebyshev polynomials.

The proposed method translates distributed parameter systems into lumped parameter systems by casting state functions into the space spanned by Chebyshev polynomials, and identification can be made with the algorithm of least square.

Piezoelectric Energy Harvesting provides the first comprehensive treatment of distributed-parameter electromechanical modelling for piezoelectric energy harvesting with extensive case studies including experimental validations, and is the first book to address modelling of various forms of excitation in piezoelectric energy harvesting, ranging.

Adaptive Identification and Control of Uncertain Systems with Nonsmooth Dynamics reports some of the latest research on modeling, identification and adaptive control for systems with nonsmooth dynamics (e.g., backlash, dead zone, friction, saturation, etc). The authors present recent research results for the modelling and control designs of.

The CACHE Virtual Process Control Book is intended to provide information on a variety of topics of interest to an undergraduate and/or graduate course on process dynamics and control.

Control of lumped and distributed parameter systems; State estimation, Kalman filter, stochastic system control Nonlinear Process Identification (Ron. Book Description. Adaptive Learning Methods for Nonlinear System Modeling presents some of the recent advances on adaptive algorithms and machine learning methods designed for nonlinear system modeling and identification.

Real-life problems always entail a certain degree of nonlinearity, which makes linear models a non-optimal choice. The modelling approach is done for distributed systems as well as other physical complex systems.

Chapter Contents: Approaches to mathematical modelling ; Methods for the development of mathematical models ; Modelling a system that exists, based on data obtained by experimentation ; Construction of models from theoretical.

Parameter identification problems for delay systems motivated by examples from aerody- namics and biochemistry are considered.

The problem of estimation of the delays is included. Using approximation results from semigroup theory, a class of theoretical approximation schemes is developed and two specific cases (“averaging” and “spline” methods) are shown to be Cited by: Control theory deals with the control of continuously operating dynamical systems in engineered processes and machines.

The objective is to develop a control model for controlling such systems using a control action in an optimum manner without delay or overshoot and ensuring control l theory is subfield of mathematics, computer science and control engineering.

Assuncao V Automotive systems identification Proceedings of the 17th IASTED international conference on Modelling and simulation, () Jiang G, Chen H and Yoshihira K () Modeling and Tracking of Transaction Flow Dynamics for Fault Detection in Complex Systems, IEEE Transactions on Dependable and Secure Computing,( "For contributions to parameter identification and adaptive control of stochastic systems" Wei Zheng "For contributions to signal processing and system identification" Michael A.

Demetriou "For contributions to estimation and optimization of distributed parameter systems" Sanjay Lall "For contributions to control of networked. Relevant topics for the symposium program include: Identification of linear and multivariable systems, identification of nonlinear systems, including neural networks, identification of hybrid and distributed systems, Identification for control, experimental modelling in process control, vibration and modal analysis, model validation, monitoring Author: Paul Van Den Hof.

This paper presents a second order P-type iterative learning control (ILC) scheme with initial state learning for a class of fractional order linear distributed parameter systems.

First, by analyzing the control and learning processes, a discrete system for P-type ILC is established, and the ILC design problem is then converted to a stability problem for such a discrete by: 1. The first parts present the basic principles, methodology, systems theory, parameter estimation, system identification, and optimization of mathematical modeling.

The succeeding parts discuss the features of stochastic and numerical modeling and Book Edition: 1. Simple physical models are often difficult to obtain for nonhomogeneous transport phenomena that involve complex couplings between several distributed variables, such as temperature profiles in tokamak plasmas.

Model-based current or combustion control approaches necessitate plasma models with real-time computation capabilities. This may prevent the use of classical.

Algorithms, an international, peer-reviewed Open Access journal. Dear Colleagues, In the past, several results involving fractional order operators have been reported both in theory and applications, covering different fields such as modelling, identification, estimation, control and signal processing, among others.

Gas turbines play an important role in power generation and aeroengines. An extended survey of methods associated with the control and systems identification in these engines, Dynamic Modelling of Gas Turbines reviews current methods and presents a number of new perspectives.

• Describes a total modelling and identification program for various classes. Existing system identification packages cannot handle continuous models of distributed-parameter systems and therefore cannot be used to identify TMM models.

This paper presents a Python software module for TMM modeling that includes integrated system identification by: 2. In this book: Identification of Continuous-Time Systems-Linear and Robust Parameter Estimation, Allamaraju Subrahmanyam and Ganti Prasada Rao consider CT system models that are linear in their unknown parameters and propose robust methods of estimation.

This book complements the existing literature on the identification of CT systems by. on Control of Distributed Parameter Systems.

The book reviews papers that tackle issues concerning the control of distributed parameter systems, such as modeling, identification, estimation, stabilization, optimization, and energy system. Download Dynamic Modelling of Gas Turbines: Identification, Simulation, Condition Monitoring and.

This paper studies the application of proper orthogonal decomposition (POD) to reduce the order of distributed reactor models with axial and radial diffusion and the implementation of model predictive control (MPC) based on discrete-time linear time invariant (LTI) reduced-order models.

In this paper, the control objective is to keep the operation of the reactor at a desired operating Cited by: 8. Ding F and Ding J Parameter estimation algorithms for missing-data systems Proceedings of the conference on American Control Conference, () Lioliou P, Viberg M and Coldrey M Performance analysis of relay channel estimation Proceedings of the 43rd Asilomar conference on Signals, systems and computers, ().The fundamental research of CS is directed towards the following research lines:• Data-driven modelling in dynamic networks• Modelling and control of linear parameter varying systems• Spatial-temporal multi-physics systems and model reduction• Data analytics and machine learning• Constrained and interconnected systems.She has co-authored more than 37 journal and conference papers, book chapters, and technical reports.

Ning is a senior member of the IEEE Power and Energy Society. She is also a member of NERC’s Systems Planning Impacts of Distributed Energy Resources Working Group (SPIDERWG). Ning has been a registered Professional Engineer since Dr.