Advances in Nonlinear Programming Concepts for Process Control

Biegler, L.T.

doi:10.1016/s1474-6670(17)43218-6

Cited by 8 publications

(10 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are several methods for suboptimal solutions of the NMPC problem based on quadratic programming (QP) algorithms . However, there are simple ideas, such as those proposed by De Keyser , which consider the key features in linear MPC to use free and forced response concepts.…”

Section: Proposed Hybrid Predictive Controlmentioning

confidence: 99%

A practical hybrid predictive control algorithm for a low‐temperature thermosolar plant

Álvarez

Pasamontes

Guzmán

et al. 2014

Optim Control Appl Methods

View full text Add to dashboard Cite

Summary This work presents a control approach to deal with plants formed by several interconnected subprocesses and where the interconnection between subprocesses can change during the plant operation. The change from one operation mode to another, where each operation mode is defined by a discrete variable set, implies a change in the process behaviour that must be counteracted by the control system. In this work, a practical hybrid model predictive control is proposed to take the behaviour changes related to the hybrid nature of the process into account. The proposed control algorithm is based on the adaptation of several well‐known control strategies that have been successfully applied in the industrial field. A solar thermal system (composed of a solar thermal flat collector field, one or more accumulation tanks connected in series and a gas heater) is used to show the resulting control strategy through detailed simulations. Copyright © 2014 John Wiley & Sons, Ltd.

show abstract

Section: Proposed Hybrid Predictive Controlmentioning

confidence: 99%

A practical hybrid predictive control algorithm for a low‐temperature thermosolar plant

Álvarez

Pasamontes

Guzmán

et al. 2014

Optim Control Appl Methods

View full text Add to dashboard Cite

show abstract

“…For nonlinear and linear models with constraints, it is not possible to find an analytical solution. However, several powerful optimization methods are available and are able to solve the optimization problem using iterative procedures (Gauss-Newton method, the Levenberg-Marquardt method or sequential quadratic programming) [28]. Either analytically or by using iterative procedures, the optimal input sequence can be computed along the input horizon N u [29].…”

Section: Optimization Proceduresmentioning

confidence: 99%

EPSAC‐controlled anesthesia with online gain adaptation

Niño

Keyser

Syafiie

et al. 2008

Adaptive Control & Signal

View full text Add to dashboard Cite

This paper presents the application of predictive control to drug dosing during anesthesia in patients undergoing surgery. A single-input (propofol) single-output (bispectral index (BIS)) model of the patient has been assumed for prediction. The performance of our previous strategy in drug dosing control has been improved to tackle inter-patient variability. A set of 12 patient models was studied and, in order to ensure the applicability of the proposed controller, gain adaptation in the controller is proposed. Preliminary studies have shown that due to static nonlinearity in the sigmoid curve of the patient model, feedback control is not feasible in the first part of the induction phase. Therefore, the control strategy applied in this study consists of controlling the effect site concentration (C e ) during the first phase and controlling BIS once the relation BIS/C e has been identified. The policy of switching and adapting the control strategies shows a good performance during the induction phase in simulation studies. Clinical tests have been scheduled at the Ghent University Hospital for the coming months.

show abstract

“…10 Comparing the linearized versions of successive solutions through time, set up in the form of linear programmes, one can isolate the changes in individual parameters. 11 In principle, this approach could be followed for the major processes constituting the core of an industrial economy. Altogether, it would yield a sequence of paired input-output tables, each with their appropriate matrices of input-output coefficients representing the economy's technology at the date of each table.…”

Section: The Macro-technics Of Historical Changementioning

confidence: 99%

Sporadic Innovation and Historical Continuity

Enos¹

2003

Prometheus

View full text Add to dashboard Cite

This article argues that, by appealing to technological factors, one can compare different innovations, even different economies, over time. An application is made to the development of steam engines and turbines over their history from 1700 to 2000, for which it is shown that four types of physical variables--temperatures, pressures, thermal efficiencies and power ratings--provide common measures of succeeding devices. Such measures can be incorporated in a technological analogue of an input-output system. In principle, an entire economy could be represented in technological form; and, since scientific variables are invariant through time, its evolution could be depicted in quantitative terms.Innovation, Technical Change, Economic History, Input-output Analysis,

show abstract

Advances in Nonlinear Programming Concepts for Process Control

Cited by 8 publications

References 22 publications

A practical hybrid predictive control algorithm for a low‐temperature thermosolar plant

A practical hybrid predictive control algorithm for a low‐temperature thermosolar plant

EPSAC‐controlled anesthesia with online gain adaptation

Sporadic Innovation and Historical Continuity

Contact Info

Product

Resources

About