Loop decomposition and dynamic interaction analysis of decentralized control systems

Zhu, Zhong-Xiang; Jutan, Arthur

doi:10.1016/0009-2509(95)00386-x

Cited by 19 publications

(26 citation statements)

References 20 publications

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“…11 (right). The same conclusions were obtained in [30] using loop decomposition, and they were validated by examining the responses to independent setpoint changes in both control loops. These step responses are depicted in Fig.…”

Section: Closed Loop Analysismentioning

confidence: 80%

New methods for interaction analysis of complex processes using weighted graphs

Arranz

Birk

2012

Journal of Process Control

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a b s t r a c tThe selection of the structure of a controller in large scale industry processes usually requires extensive process knowledge. The aim of this paper is to report new results on recently suggested methods for the analysis of complex processes. These methods aid the designers in comprehending a process by representing structural and functional relationships from actuators and process disturbances to measured or estimated variables. The methods are formulated in a flexible framework based on graph theory, which can also be used for closed-loop analysis. Additionally, the sensitivity of the methods to scaling and time delays are discussed and resolved. It is also proposed how filtering can be used to restrict the analysis to a frequency region of interest.The feasibility of the methods is shown by the use of three case studies. A quadruple tank process is used to exemplify the methods and their use. Then the methods are applied on a real-life process, the stock preparation plant of a pulp and paper mill. The third study case analyzes a previously published example in closed loop.It is shown that the methods can be used to take efficient decisions on decentralized and sparse control structures, as well as assessing the channel interactions in a closed-loop system.

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Section: Closed Loop Analysismentioning

confidence: 80%

New methods for interaction analysis of complex processes using weighted graphs

Arranz

Birk

2012

Journal of Process Control

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“…In fact, some authors 5,15,18 have shown that multivariable closed-loop systems can be represented with the transmittances of figure 1. Figure 1 Direct, parallel, and interaction transmittances in a multivariable closed-loop system…”

Section: Generalized Relative Dynamic Gainsmentioning

confidence: 99%

“…Interaction is an important problem mainly with decentralized control. Several authors have proposed different methods to measure the interaction of multivariable processes 1,[4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] . Among them, Witcher and McAvoy 7 and Bristol 8 have suggested an extension of the well-known relative gain array (RGA) 6 to the entire frequency domain by defining the relative dynamic gain array (RDGA).…”

Section: Introductionmentioning

confidence: 99%

A New Representation of the Generalized Relative Dynamic Gains

Gagnon

Pomerleau

Desbiens

2003

AIAA Guidance, Navigation, and Control Conference and Exhibit

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A new representation of the generalized relative dynamic gains (GRDG) for input-output pairing of two input-two output (TITO) processes is proposed. This new tool permits a determination of the best specifications for each closed-loop in order to minimize the interaction between the loops when using decentralized control. An application of the new GRDG representation is used to clearly show that the pairing which minimizes the interaction, can depend on the selected specifications. This new tool could be used with aerial systems (missiles, planes, unmanned aerial vehicles, etc.).

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“…This concept was further developed by Zhu and Jutan (1996), where loop interaction can be exposed by investigating the response of an output to its input in the presence of interaction from other loops through the hidden loops. This situation can be best depicted by Figure 2.3.…”

Section: ^ ) All Loops Openmentioning

confidence: 99%

“…Here, 0 y (s) is the absolute interaction, which can be obtained by the signal flow technique (Zhu and Jutan. 1996), the relative interaction can be then represented as the ratio of the absolute interaction and the independent interaction free process…”

Section: Nanyang Technological Universitymentioning

confidence: 99%

Multivariable control system design based on relative interaction energy

Xiong¹

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Table of Contents ii Summary iv List of Figures vi List of Tables viii Chapter 1 1 Introduction 1 1.1. Motivations 1 1.2. Objectives 2 1.3. Major contributions of the thesis 2 1.4. Organization of the thesis 3 Chapter 2 Literature Review 2.1. Introduction 5 2.2. Preliminaries 2.3. Interaction analysis and loop pairing 2.3.1. RGA based loop paring 12 2.3.2. RIA based loop paring 2.3.3. Dynamic RGA (DRGA) based loop paring 2.3.4. GRDG based loop paring 2.3.5. SSV based loop paring 2.4. Multi-loop controller design 2.4.1. Detuning methods 2.4.2. Sequential loop closing methods 2.4.3. Independent design methods 2.4.4. Simultaneous equation solving methods 2.5. Decoupling controller design 2.5.1. Ideal/dynamic decoupling methods 2.5.2. Static decoupling methods 39 2.5.3. Partial/block decoupling methods References 43 Chapter 3 49 Interaction Analysis and Loop Pairing for Multivariate Processes 49 3.1. Introduction 49 3.2. Preliminaries 51 3.3. A modified loop pairing rule 52 3.4. Case studies 58 3.5. Conclusions 61 References 61 Chapter 4 63 Decentralized Control System Design 63

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Loop decomposition and dynamic interaction analysis of decentralized control systems

Cited by 19 publications

References 20 publications

New methods for interaction analysis of complex processes using weighted graphs

New methods for interaction analysis of complex processes using weighted graphs

A New Representation of the Generalized Relative Dynamic Gains

Multivariable control system design based on relative interaction energy

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