Advances in nonlinear programming concepts for process control

Biegler, L.T.

doi:10.1016/s0959-1524(98)00009-2

Cited by 55 publications

(28 citation statements)

References 15 publications

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“…Riccati recursions have also been used together with active set methods for solving the optimal control problem, (Arnold and Puta, 1994;Glad and Jonson, 1984). Comparisons between active set methods and IP methods have been done by several authors, (Albuquerque et al, 1997;Biegler, 1997;Wright, 1996). Recently, it has been shown that stability of MPCs can be established less conservatively if the optimization problem considered at each sampling interval is a Quadratically Constrained Quadratic Program (QCQP), (Lee, 2000;Lee and Kouvaritakis, 1999;Scokaert and Rawlings, 1998).…”

mentioning

confidence: 99%

Efficient Solution of Second Order Cone Program for Model Predictive Control

Åkerblad

Hansson

2002

IFAC Proceedings Volumes

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mentioning

confidence: 99%

Efficient Solution of Second Order Cone Program for Model Predictive Control

Åkerblad

Hansson

2002

IFAC Proceedings Volumes

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“…For nonlinear and linear models with constraints, it is not possible to find an analytical solution. However, there are several powerful optimization methods to solve the optimization problem using iterative procedures (Gauss-Newton and LevenbergMarquardt methods or Sequential Quadratic Programming) (Biegler;1998).…”

Section: Optimization Proceduresmentioning

confidence: 99%

Predictive Control With Robust Dead-Time Compensation: Application to Drug Dosing During Anesthesia

Torrico

Keyser

Ionescu

et al. 2007

IFAC Proceedings Volumes

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This paper presents the application of predictive control to drug dosing during anesthesia in patients undergoing surgery. A single-input (Propofol) singleoutput (Bispectral index) model of the patient has been assumed which includes the variable dead time caused by the measurement of the Bispectral index. A set of 12 patient models were studied. Inter-patient variability, varying dead time and disturbances are used to assess robustness of the controller. Furthermore, the controller guarantees stability under the operation conditions. Simulation results illustrate the use of the proposed controller.

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“…Constrained nonlinear programming technique is very famous for its application in many fields of science such as chemical engineering (Bailey et al 1993;Dantzing 1964), control theory (Biegler 1998), decision theory (Steuer 1986) ... etc. In the present work this technique is applied to the inversion and interpretation of geophysical SP data.…”

Section: Frank and Wolfe Algorithm For The Inversion Of Sp Datamentioning

confidence: 99%

A Constrained Nonlinear Programming Technique for Interpretation of Self-Potential Anomalies due to Two-Dimensional Inclined Sheets of Finite Depth Extent

Asfahani¹,

Tlas²

2005

ear

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ABSTRACT. A new and simple method based on constrained nonlinear programming concepts is being proposed in this research to interpret the self-potential (SP) anomaly due to two-dimensional inclined sheets of finite depth extent. A constrained nonlinear model has been formulated to describe the geophysical problem related to this buried inclined structure. Thereafter this model has been solved by the famous Frank and Wolfe algorithm in order to estimate the geophysical parameters of the modeled sheets which are: the depth of upper and lower edges h and H, inclination angle θ, and electrical dipole moment k. This algorithm is chosen for being robust and also because its application to SP data converges rapidly towards the optimal estimation of parameters. To prove the efficiency of this new constrained nonlinear programming technique, three theoretical models with a random noise of 2% have been studied, and as a result, a very close and satisfactory agreement has been obtained between the assumed and evaluated parameters. The validity of this new proposed method is tested on a practical field example from India, where available SP data existed and has been previously analyzed by a spectral analysis approach. Moreover, a statistical analysis has been carried out to confirm the superiority of this method in comparison with that suggested by Atchuta et al. (1982). All acquired results indicate clearly its precision and accuracy. The advantages of the proposed technique have also been discussed and demonstrated comparing them with other published interpretative techniques.

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Advances in nonlinear programming concepts for process control

Cited by 55 publications

References 15 publications

Efficient Solution of Second Order Cone Program for Model Predictive Control

Efficient Solution of Second Order Cone Program for Model Predictive Control

Predictive Control With Robust Dead-Time Compensation: Application to Drug Dosing During Anesthesia

A Constrained Nonlinear Programming Technique for Interpretation of Self-Potential Anomalies due to Two-Dimensional Inclined Sheets of Finite Depth Extent

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