Control-relevant prefiltering: a systematic design approach and case study

Rivera, Daniel E.; Pollard, I.F.; García, Camino López

doi:10.1109/9.148348

Cited by 78 publications

(38 citation statements)

References 5 publications

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“…Consequently, there is considerable interest in process identification (or system identification), namely, developing empirical dynamic models from input-output data. Typically, process identification is the most demanding and time consuming step in the industrial implementation of advanced control strategies [196].…”

Section: Multivariable Control Strategiesmentioning

confidence: 99%

A Perspective on Advanced Strategies for Process Control (Revisited)

Seborg

1999

Advances in Control

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Summary. Th is paper provides a personal perspective on the current status of advanced process control. First, process control strategies are classified according to the extent to which they have been applied in industry. Then important new developments in information technology and plant automation are summarized. Prominent advanced process control methods are critiqued with emphasis placed on key issues and unresolved research problems. Finally, recent developments in an important related field, process monitoring, are reviewed.

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Section: Multivariable Control Strategiesmentioning

confidence: 99%

A Perspective on Advanced Strategies for Process Control (Revisited)

Seborg

1999

Advances in Control

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“…The above idea is the basis in Rivera, Pollard, and Garcia (1992) where, for numerical reasons, an iterative procedure is proposed where the estimated model from iteration k − 1 is used in the noise model at iteration k.…”

Section: Prefiltering Methodsmentioning

confidence: 99%

From experiment design to closed-loop control

2005

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The links between identification and control are examined. The main trends in this research area are summarized, with particular focus on the design of low complexity controllers from a statistical perspective. It is argued that a guiding principle should be to model as well as possible before any model or controller simplifications are made as this ensures the best statistical accuracy. This does not necessarily mean that a full-order model always is necessary as well designed experiments allow for restricted complexity models to be near-optimal. Experiment design can therefore be seen as the key to successful applications. For this reason, particular attention is given to the interaction between experimental constraints and performance specifications.

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“…The general idea is to find the best nominal model for controller design, instead of the model that can minimize the open loop prediction error of the experiment data. Although control-relevant nominal models can be found through a variety of "identification for control" approaches [1], [2], [3], there is no guarantee that the controller designed from the identified model will have a satisfactory performance with a set of models within an estimated model uncertainty region.…”

Section: Introductionmentioning

confidence: 99%

System Identification for Robust Control

Zhan

Tsakalis

2007

2007 American Control Conference

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This paper presents a robust-control-oriented system identification method aiming to minimize the normalized coprime factor uncertainty of the performance-weighted system. The nominal model and the normalized coprime factor uncertainty bound are estimated employing a filter bank approach, which approximately calculates the chordal distance between the identified model and the true system in the frequency range of interest. An iterative LMI optimization is formulated to identify the model coefficients. The optimal stability margin is also calculated and compared to the identified nominal uncertainty bound to decide if re-identification is necessary.

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Control-relevant prefiltering: a systematic design approach and case study

Cited by 78 publications

References 5 publications

A Perspective on Advanced Strategies for Process Control (Revisited)

A Perspective on Advanced Strategies for Process Control (Revisited)

From experiment design to closed-loop control

System Identification for Robust Control

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