Process controllability toolbox (PCTB)

Fararooy, S.; Perkins, John D.; Malik, Tahir I.; Oglesby, M.J.; Williams, Simon C.

doi:10.1016/0098-1354(93)80049-s

Cited by 20 publications

(3 citation statements)

References 13 publications

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“…The performance indicators of bioreactor could be divided into two categories; (1) steady-state performance in terms of product yield, conversion and productivity, and (2) dynamic controllability i.e. how easy to control the system at desired conditions (Fararooy et al, 1993). In the chemical process industries, dynamic controllability is one of the key issues that determine plant profitability.…”

Section: Framework Of Multi-scale Modelingmentioning

confidence: 99%

Multi-scale Framework for Modeling and Control of Fermentation Processes

Nandong

Samyudia

Tadé

2008

IFAC Proceedings Volumes

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Section: Framework Of Multi-scale Modelingmentioning

confidence: 99%

Multi-scale Framework for Modeling and Control of Fermentation Processes

Nandong

Samyudia

Tadé

2008

IFAC Proceedings Volumes

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“…Ziegler and Nichols [56] had already observed that for a "miserably designed process" even the finest controller may not deliver the desired performance. Hence, already in the process design phase it is important to have a thorough understanding of how a process unit will limit the achievable performance of the controlled unit [2], [11], [31], [43], [45]. Obviously, such an understanding is also important for controller design [7], [29], [33], [35], [42], [46], [56].…”

Section: Introductionmentioning

confidence: 99%

Finite-time behavior of inner systems

Ludlage

Weiland

Stoorvogel

et al. 2003

IEEE Trans. Automat. Contr.

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In this paper, we investigate how nonminimum phase characteristics of a dynamical system affect its controllability and tracking properties. For the class of linear time-invariant dynamical systems, these characteristics are determined by transmission zeros of the inner factor of the system transfer function. The relation between nonminimum phase zeros and Hankel singular values of inner systems is studied and it is shown how the singular value structure of a suitably defined operator provides relevant insight about system invertibility and achievable tracking performance. The results are used to solve various tracking problems both on finite as well as on infinite time horizons. A typical receding horizon control scheme is considered and new conditions are derived to guarantee stabilizability of a receding horizon controller.

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“…It is therefore important to have a detailed understanding of which characteristics of a process limit achievable performance of the controlled unit [1,6,14,20,21] and how these characteristics effect the controller design [ 4,13,15,17,19,22,27].…”

Section: Introductionmentioning

confidence: 99%

On finite-time control of inner systems

Ludlage

Weiland

Stoorvogel

1999

Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251)

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It is well known that non-minimum phase zeros of dynamical systems restrict the achievable performance of closed-loop transfer functions. If performance is specified in terms of finite time properties, then it is not evident that the zero structure of transfer functions limits the achievable performance of the controlled system. In this paper it is shown that the Hankel singular values of the inner factor of the system transfer function provide relevant information for achievable (finite time) performance of the controlled process.

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Process controllability toolbox (PCTB)

Cited by 20 publications

References 13 publications

Multi-scale Framework for Modeling and Control of Fermentation Processes

Multi-scale Framework for Modeling and Control of Fermentation Processes

Finite-time behavior of inner systems

On finite-time control of inner systems

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