Robust controller design methodology for multivariable chemical processes

Agamennoni, Osvaldo; Desages, A.; Romagnoli, José A.

doi:10.1016/0009-2509(88)80047-2

Cited by 15 publications

(4 citation statements)

References 6 publications

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“…To provide a comparison independent of the type of controller used, we implemented an 'ideal' nominal controller (Agamennoni et al 1988). This controller is obtained by multiplying the plant inverse by a filter to guarantee nominal stability and acceptable closed-loop performance.…”

Section: Applicationsmentioning

confidence: 99%

Trade-offs in robust controller design

Figueroa

Desages

Palazoğlu

et al. 1993

International Journal of Control

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In the process of designing robust control systems, the designer often finds that the robust stability and robust performance conditions are not met for the problem as originally posed. Then, it becomes imperative to determine the next step in the control design study so that an effective search is initiated. This can be unequivocally accomplished by a trade-off analysis. Based on the structured singular value paradigm, we present a method that helps the control designer assess the relative importance of information regarding the feedback system. Such information typically consists of process knowledge through the model, control system complexity quantified by the controller order and performance requirements as set forth by the designer. The trade-off analysis is built into the robustness criterion, and simulation examples are utilized to demonstrate the effectiveness of this approach. IntroductionIn recent years, significant effort has focused on feedback control problems originating from the mismatch between the actual plant and the model used for controller design. This so-called robustness issue has been recognized for quite some time (Horowitz and Sidi 1972) and in the last decade, Doyle and Stein (1981) constructed the robust stability and performance conditions using the spectral norms. Doyle (1982) extended his approach to cover more structured uncertainty descriptions and defined the structured singular value (/1). Parallel to this, process control literature also witnessed similar trends in handling the ever-present issue of robustness. The reader is referred to a special issue of Computers and Chemical Engineering (1989) as well as to a book by Morari and Zafiriou (1989) for an in-depth account of the area of robust process control.The robust control design process typically proceeds by compiling all the available information regarding the process and the designer's expectations about the overall feedback response. The former consists of the process model along with some estimate of the model uncertainty. The latter includes performance specifications such as speed of response, offset-free tracking etc. To this list, one could also add certain specifications regarding the controller structure. Possible scenarios would include single-loop and multivariable controllers as well as low order controllers like the ones with proportional-integral-derivative (PI D) elements. This would limit the order of the controller during implementation and one should weigh the consequences of approximating high-order controllers with low-order ones within the context of the robust control design.

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Section: Applicationsmentioning

confidence: 99%

Trade-offs in robust controller design

Figueroa

Desages

Palazoğlu

et al. 1993

International Journal of Control

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“…Multiloop controllers for the Cj-splitter column were determined using a more general robust multivariable controller design methodology (Agamennoni et al 1988 and1992) which is described briefly below.…”

Section: Robust Multiloop Controlmentioning

confidence: 99%

Advanced controller design for a distillation column

Agamennoni

Figueroa

Barton

et al. 1994

International Journal of Control

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This paper considers the design of an advanced (relative to conventionally tuned multiloop PI control) control scheme for an industrial Cj-splitter column using a range of recently proposed analytic tools. Due to product specifications, this column must be kept under tight control despite the effects of large disturbances and slow system response. Model predictive control was shown to provide a substantial improvement over a conventionally tuned multiloop controller but required a level of computation beyond that possible in a basic distributed control system. Controlled column behaviour comparable with that possible with model predictive control was shown to be achievable using a multiloop controller specifically tuned so as to incorporate disturbance rejection capabilities. The value of such controller design tools and their ready implementation within commercial software (here Matlab) should see their more widespread acceptance and use by the industrial control community.

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“…Because of this, many methods for multivariable control (MVC) have been reported. These methods include inverse Nyquist array, characteristic-locus, inverse-based decoupling controls, − internal model controls, − multivariable predictive control, − dynamic matrix control (DMC), and quadratic dynamic matrix control (QDMC) . Many of the above-mentioned methods tried to design the open-loop transfer function to be strictly or roughly diagonally dominant, so as to reduce the effect of the interaction.…”

Section: Introductionmentioning

confidence: 99%

Multivariable Control with Generalized Decoupling for Disturbance Rejection

Lin

Jeng

Huang

2009

Ind. Eng. Chem. Res.

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In this paper, a systematic procedure to design multivariable controllers that have options for selective decoupling of different structures (e.g., full or partial decoupler) is proposed. The controller structure is determined based on an analysis of relative load gain (RLG). For controller design, the adjoint of a template matrix is provided to design the MIMO decoupler that is needed for a given process. In the case where decoupling is desirable, according to the number of zero deficiency that the adjoint of the template matrix has, a pole-zero compensator is incorporated to preserve the properness of the decoupling controller and to define the decoupled open-loop dynamics. By incorporating the decoupler, the controller in the main loop is synthesized for disturbance rejection. Stability robustness of the system is tuned using measures of modeling errors in the decoupled open-loop process. Simulation examples are used to illustrate the proposed method and show its effectiveness in disturbance rejection in MIMO systems.

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Robust controller design methodology for multivariable chemical processes

Cited by 15 publications

References 6 publications

Trade-offs in robust controller design

Trade-offs in robust controller design

Advanced controller design for a distillation column

Multivariable Control with Generalized Decoupling for Disturbance Rejection

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