MIMO interaction measure and controller structure selection

Salgado, Mario E.; Conley, Arthur

doi:10.1080/0020717042000197631

Cited by 124 publications

(80 citation statements)

References 11 publications

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“…Recent references that compile classical results related to the first question can be found in (Conley, 2000), (Salgado and Conley, 2004) and in the references therein. The main drawback of classical interaction measures (RGA, DRGA, NI) is that they do not consider the dynamics of the process, since their either rely only on the frequency response at DC or, when the complete frequency axis is taken into account (as the DRGA does), they lead to results which are hard to analyze and to use.…”

Section: Introductionmentioning

confidence: 99%

“…Those advantages originate in their simplicity, their connection with classical single-input single-output control and in implementation and tuning issues (Conley, 2000), (Yuz and Goodwin, 2003). Nevertheless, that approach to control system design poses several fundamental questions (Sourlas and Manousiousthakis, 1995), (Goodwin et al, 1999), (Salgado and Conley, 2004). Among those questions are, firstly, how to select those measurements that will be used to generate every plant input, i.e., how to pair inputs and outputs, and secondly, once the pairing has been decided, how to synthesize, if possible, a stabilizing controller.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, most of that work is restricted to the 2 × 2 case and only in simple cases they help in the higher dimensional structure selection problem. A new tool called the participation matrix (PM) based on Gramians has been proposed in (Salgado and Conley, 2004). This interaction measure condenses information of the dynamics of the plant in a set of real numbers, allowing to easy identify the interaction structure of a given plant, thus providing useful information to decide on the input-output pairing.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Class of Optimal Decentralized Controllers for Open Loop Stable Processes

Brocal

Vera

2005

IFAC Proceedings Volumes

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Class of Optimal Decentralized Controllers for Open Loop Stable Processes

Brocal

Vera

2005

IFAC Proceedings Volumes

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“…The second category of the interaction measures is the family of the gramian based methods. A method from this category was first proposed in [11] and further in [12]. In this category, the observability and the controllability gramians are used to form the Participation Matrix (PM).…”

Section: Introductionmentioning

confidence: 99%

Control configuration selection for multivariable descriptor systems

Shaker

Stoustrup

2012

2012 American Control Conference (ACC)

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Abstract² Control configuration selection is the procedure of choosing the appropriate input and output pairs for the design of SISO (or block) controllers. This step is an important prerequisite for a successful industrial control strategy. In industrial practices it is often the case that the system, which is needed to be controlled, is either in the descriptor form or can be represented in the descriptor form. Singular systems and the differential algebraic equation (DAE) systems are among these systems. Descriptor systems appear in the variety of fields to describe the practical processes ranging from power systems, hydraulic systems to heat transfer, and chemical processes. The focus of this paper is on the problem of control configuration selection for multivariable descriptor systems. A gramianbased interaction measure for control configuration selection of such processes is described in this paper. The proposed MIMO interaction measure is the extension of its gramian-based analogous counterpart, which has been proposed for the input± output pairing as well as for the controller architecture selection of the processes with the standard state-space form. The main advantage of this interaction measure is that it can be used to propose a richer sparse or block diagonal controller structure. The interaction measure is used for control configuration selection of the linearized CSTR model with descriptor from.

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“…The IM considered in this paper is the Participation Matrix (PM) [1], which uses the squared HilbertScmhmidt (HS) norm.…”

Section: Introductionmentioning

confidence: 99%

Bounds on a gramian-based Interaction Measure for robust control structure selection

Miguel

Birk

2011

2011 9th IEEE International Conference on Control and Automation (ICCA)

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Abstract-This paper reviews a method for approximating the bounds on a gramian-based Interaction Measure (IM) for systems described by uncertain parametric models. The considered IM is the Participation Matrix. The reviewed method is based on analyzing the uncertainty in the area enclosed by the Nyquist curve. Conditions for the exactitude of this method are derived, and implementation issues are discussed.It is shown that the reviewed method is applicable to nonparametric uncertain models of the frequency response function, which enables the estimation of the indicator from process data including the uncertainty bounds.Using these bounds, robust control structure selection can be performed.

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MIMO interaction measure and controller structure selection

Cited by 124 publications

References 11 publications

A Class of Optimal Decentralized Controllers for Open Loop Stable Processes

A Class of Optimal Decentralized Controllers for Open Loop Stable Processes

Control configuration selection for multivariable descriptor systems

Bounds on a gramian-based Interaction Measure for robust control structure selection

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