Combined internal model and inferential control of a distillation column via closed-loop identification

Häggblom, Kurt E.

doi:10.1016/0959-1524(95)00032-1

Cited by 11 publications

(3 citation statements)

References 14 publications

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“…Another related idea is inferential control. 8 However, in inferential control the basic idea is to use the measurements y to estimate the primary variables y 1 , whereas the objective of indirect control is to directly control a combination c of the measurements y. Haggblom 9 proposed a combined internal model and inferential control structure.…”

Section: Introductionmentioning

confidence: 99%

Perfect Steady-State Indirect Control

Hori

Skogestad

Alstad

2005

Ind. Eng. Chem. Res.

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Indirect control is commonly used in industrial applications where the primary controlled variable is not measured. This paper considers the case of "perfect indirect control" where one attempts to control a combination of the available measurements such that there is no effect of disturbances on the primary outputs at steady-state. This is always possible provided the number of measurements is equal to the number of independent variables (inputs plus disturbances). It is further shown how extra measurements may be used to minimize the effect of measurement error. The results in this paper also provide a nice link to previous results on inferential control, perfect disturbance rejection and decoupling (DRD), and self-optimizing control.

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Section: Introductionmentioning

confidence: 99%

Perfect Steady-State Indirect Control

Hori

Skogestad

Alstad

2005

Ind. Eng. Chem. Res.

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“…As above, possible remedies (in-cluding the same problems and difficulties) are to let the algorithm employ several linear process models, each valid at a given operating point, or a nonlinear process model valid over the whole operating range.In this article, multivariable nonlinear and adaptive control is applied to a binary distillation column (Waller et al, 1988). Previous investigations (Waller et al, 1988;Sandelin et al 1991;Haggblom, 1993) have shown that the process has nonlinear dynamics. Although linear controllers can be used to control the process at any given operating point, it is difficult to control the process satisfactorily over the whole operating range of interest using a fixed linear control law.…”

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confidence: 99%

Multivariable nonlinear and adaptive control of a distillation column

et al. 1995

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In chemical process control, the processes typically exhibit nonlinear behavior. In spite of this, linear feedback laws often perform quite well as long as the process is being controlled at a fixed operating point. Linear controller designs based on linearized process models are therefore commonly used. The nonlinear process dynamics can be taken into account implicitly by treating them as part of the model uncertainties.If the nonlinearity of the process is too severe for a (single) linear control law to perform satisfactorily, one possibility is to describe the process by a set of different linear models, each valid at a given operating point. One can then derive a different linear feedback law for each operating point and use the feedback law corresponding to the prevailing operating point (Shamma and Athans, 1991). A potential difficulty with this approach is to decide which model and feedback law to use between the given set of operating points. Another possibility is to describe the process by a nonlinear model valid over the whole operating range of interest. The main difficulty with this approach is to find a suitable nonlinear model. Models derived from physical principles are in general much too complex for controller design, and therefore simpler models that capture the nonlinearities are required. If a nonlinear model is available, one can design a nonlinear controller based on this model for the whole operating range (Slotine and Li, 1991). However, it is also possible to use a linear controller whose parameters are updated at each sampling instant on the basis of a linearized form of the nonlinear model.For processes with poorly known or slowly time-varying dynamics, adfptive and self-tuning control are widely accepted approaches ( Astrom and Wittenmark, 1989). When applying adaptive control to nonlinear processes, care should be taken to handle the nonlinearities properly. Adaptive controllers are usually based on linear process models and linear design methods. Although adaptive controllers have the property of adapting their behavior to changing process properties, it is, however, somewhat unsatisfactory to not consider the fact that the process is nonlinear, and to let the adaptive controller retune itself (and the parameters of the linear model) whenever the operating point is changed. As above, possible remedies (including the same problems and difficulties) are to let the algorithm employ several linear process models, each valid at a given operating point, or a nonlinear process model valid over the whole operating range.In this article, multivariable nonlinear and adaptive control is applied to a binary distillation column (Waller et al., 1988). Previous investigations (Waller et al., 1988;Sandelin et al. 1991;Haggblom, 1993) have shown that the process has nonlinear dynamics. Although linear controllers can be used to control the process at any given operating point, it is difficult to control the process satisfactorily over the whole operating range of interest using a fixed linear co...

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“…This Moreover, it has become more appealing for closed-loop identification test, in order to facilitate online tuning of the closed-loop controller [15]- [17]. Based on closed-loop step test in terms of the internal model control (IMC) structure, Häggblom K. E. [18] demonstrated that closed-loop identification facilitates better representation of the process dynamic response characteristics for closed-loop operation. Using a proportional (P) or proportional-integral-derivative (PID) type controller for closed-loop step test, Wang and Cluett [19] developed an identification algorithm to obtain an continuous-time Laguerre model; Cai, Fang and Wang [20] reported a SOPDT identification algorithm from the analysis of closed-loop frequency response; Using first-order Taylor approximation for the time delays in individual channels, Li et al [21] presented a LS fitting algorithm for multivariable processes.…”

Section: Introductionmentioning

confidence: 99%

Identification of low-order process model with time delay from closed-loop step test

Liu

Gao

Zhao

2009

Proceedings of the 48h IEEE Conference on Decision and Control (CDC) Held Jointly With 2009 28th Chinese Control Conference

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Abstract-To facilitate control-oriented model identification during closed-loop system operation, a low-order model identification method is proposed in this paper, based on using closed-loop step response test. By introducing a damping factor to the closed-loop step response for realization of the Laplace transform, a frequency response estimation algorithm is developed in terms of the closed-loop control structure used for identification. Correspondingly, two model identification algorithms are derived analytically for obtaining the widely used low-order process models of first-order-plus-dead-time (FOPDT) and second-order-plus-dead-time (SOPDT), respectively. Illustrative examples from the recent literature are given to demonstrate the effectiveness of the proposed identification algorithms.

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Combined internal model and inferential control of a distillation column via closed-loop identification

Cited by 11 publications

References 14 publications

Perfect Steady-State Indirect Control

Perfect Steady-State Indirect Control

Multivariable nonlinear and adaptive control of a distillation column

Identification of low-order process model with time delay from closed-loop step test

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