Control of constrained multivariable nonlinear process using a two-phase approach

Jang, Shi-Shang; Joseph, Babu; Mukai, H.

doi:10.1021/ie00070a029

Cited by 19 publications

(9 citation statements)

References 5 publications

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“…However, a closed-loop observer should be used to provide better performance in the presence of model uncertainty as demonstrated by a number of case studies (Sistu and Bequette, 1991;Jang et al, 1987). Extension to the case of a closed-loop observer may be carried out in the same framework and is an area for further research.…”

Section: Incomplete State Informationmentioning

confidence: 99%

“…Garcia et al (1989) provide a nice review of LMPC methods. Recognizing that the use of linear models overly restricted control system performance, a number of researchers extended LMPC techniques to incorporate a nonlinear model (Garcia, 1984;Jang et al, 1987;Sistu and Bequette, 1991;Eaton and Rawlings, 1992;Li and Biegler, 1989;Brengel and Seider, 1989). A review of nonlinear process control (including nonlinear model-predictive control (NMPC)) is provided by Bequette (1991).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Nonlinear model‐predictive control: Closed‐loop stability analysis

Sistu

Bequette

1996

AIChE Journal

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Section: Incomplete State Informationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nonlinear model‐predictive control: Closed‐loop stability analysis

Sistu

Bequette

1996

AIChE Journal

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“…lb) with a constrained nonlinear optimization program: Sequential Simultaneous Linear approximation. The sequential solution techniques involves the iterative solution of the ordinary differential equations (ODE's) as an "inner loop" to evaluate the objective function (Jang et al, 1987), with an optimization code as the "outer loop." Jang et al have used the generalized reduced gradient (GRG) optimization approach with a Runge-Kutta integration code for model solution.…”

Section: Solution Of the Dynamic Model Equationsmentioning

confidence: 99%

“…Bequette (1991b) presented an approach for identification in a nonlinear, predictive control framework, while Sistu and Bequette (1990) developed a method to activate the process identification strategy. Jang et al (1987) and also presented nonlinear programming approaches for estimation. Jang et al (1986) showed that a nonlinear programming approach is superior to KaIman filtering for a nonlinear dynamic system.…”

Section: Y P R E D ( K + I ) = Y M ( K + I ) + D ( K )mentioning

confidence: 99%

“…Progress has been made recently in a number of areas mentioned above, which are limited to specific cases of SISO processes (as summarized by Bequette, 1991a). Most of the limitations to differential geometry-based techniques are avoided by using nonlinear predictive control (NLPC) techniques (Bequette, 1991b;Eaton and Rawlings, 1990a;Jang et al, 1987;Patwardhan et al, 1990), which are extensions of successful linear model-predictive control (LMPC) techniques.…”

Section: Introductionmentioning

confidence: 99%

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Nonlinear predictive control of uncertain processes: Application to a CSTR

Sistu

Bequette

1991

AIChE Journal

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Observer based nonlinear quadratic dynamic matrix control for state space and input/output models

Gattu

Zafiriou

1995

Can J Chem Eng

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An observer based nonlinear Quadratic Dynamic Matrix Control (QDMC) algorithm is developed for use with nonlinear input-output (I/O) and state space models. It generalizes and extends previously published nonlinear QDMC algorithms. The extension to I/O models is particularly important due to the increased use of neural networks and other types of nonlinear black box models in the chemical industry. Disturbance rejection and offset free tracking is addressed in a general setting utilizing concepts from filtering theory. Various kinds of disturbance models can be incorporated in the formulation. Even though nonlinear models are utilized for model prediction, the on-line optimization is formulated as a single Quadratic Program, thus preserving the computational advantages of nonlinear QDMC as compared to Model Predictive Control algorithms based on nonlinear programming techniques. The examples illustrate parameter tuning for open-loop unstable and stable processes and point out both benefits and shortcomings of the algorithm.On a mis au point un algorithme de contr8le matriciel dynamique quadratique (QDMC) non linhire bast sur un observateur destint aux modbles entr&/sortie (E/S) non lineaires et aux modkles d'espace des ttats. Cet algorithme gCntralise et &end les algorithmes QDMC non lineaires publits andrieurement. Cette gtntralisation aux modkles E/S est particulitrement importante du fait de I'utilisation accrue des rtseaux neuronaux et d'autres types de modtles boite noire non linhires dans I'industrie chimique. La rtjection et le suivi sans compensation des perturbations est considtrk dans le contexte gCntral de la thbrie du filtrage. Diffkrents types de modbles de perturbation peuvent &tre introduits dans la formulation. M&me si des modbles non lineaires sont utilids pour la prkdiction des modbles, I'optimisation en ligne est effectuk avec un programme quadratique unique, ce qui permet de conserver les avantages du calcul du QDMC non linhire comparativement aux algorithmes de contrdle prkdictif des modbles b&s sur des techniques de programmation non linhires. Les exemples illustrent I'ajustement des parambtres pour des prockdts en boucle ouverte stables ou instables et soulignent tant les avantages que les limites de I'algorithme.

show abstract

Control of constrained multivariable nonlinear process using a two-phase approach

Cited by 19 publications

References 5 publications

Nonlinear model‐predictive control: Closed‐loop stability analysis

Nonlinear model‐predictive control: Closed‐loop stability analysis

Nonlinear predictive control of uncertain processes: Application to a CSTR

Observer based nonlinear quadratic dynamic matrix control for state space and input/output models

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