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Optimal PI and PID control of first-order plus delay processes and evaluation of the original and improved SIMC rules

Journal of process control, 2018-10, Vol.70, p.36-46

2018

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Advanced control using decomposition and simple elements

Annual reviews in control, 2023, Vol.56, p.100903, Article 100903

Skogestad, Sigurd

2023

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Real-Time Optimization under Uncertainty Applied to a Gas Lifted Well Network

Processes, 2016-12, Vol.4 (4), p.52-52

2016

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Comparison of Model-Based Control Solutions for Severe Riser-Induced Slugs

Energies (Basel), 2017-12, Vol.10 (12), p.2014

2017

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Primal–dual feedback-optimizing control with override for real-time optimization

Journal of process control, 2024-06, Vol.138, Article 103208

2024

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Simple analytic rules for model reduction and PID controller tuning

Journal of process control, 2003-06, Vol.13 (4), p.291-309

Skogestad, Sigurd

2003

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A sensory-motor control model of animal flight explains why bats fly differently in light versus dark

PLoS biology, 2015-01, Vol.13 (1), p.e1002046-e1002046

2015

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A Rigorous Model for Evaluating Moving Window Soft Sensors for Industrial Distillation Processes

Chemical engineering transactions, 2018-10, Vol.69

2018

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Online Process Optimization with Active Constraint Set Changes using Simple Control Structures

Industrial & engineering chemistry research, 2019-07, Vol.58 (30), p.13555-13567

2019

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Simple analytic rules for model reduction and PID controller tuning

Modeling, identification and control, 2004-04, Vol.25 (2), p.85-120

SKOGESTAD, Sigurd

2004

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Linear parameter-varying model for a refuellable zinc-air battery

Royal Society open science, 2020-12, Vol.7 (12), p.201107-201107

2020

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Dividing Wall Columns for Natural Gas Liquefaction Plants

Chemical engineering transactions, 2018-10, Vol.69

2018

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The setpoint overshoot method: A simple and fast closed-loop approach for PID tuning

Journal of process control, 2010-12, Vol.20 (10), p.1220-1234

2010

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The use of first principles model for evaluation of adaptive soft sensor for multicomponent distillation unit

Chemical engineering research & design, 2019-11, Vol.151, p.70-78

2019

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Optimization of fixed-order controllers using exact gradients

Journal of process control, 2018-11, Vol.71, p.130-138

2018

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Tuning for Smooth PID Control with Acceptable Disturbance Rejection

Industrial & engineering chemistry research, 2006-11, Vol.45 (23), p.7817-7822

Skogestad, Sigurd

2006

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Economically efficient operation of CO2 capturing process. Part II. Design of control layer

Chemical engineering and processing, 2012-02, Vol.52, p.112-124

2012

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Null Space Method for Selecting Optimal Measurement Combinations as Controlled Variables

Industrial & engineering chemistry research, 2007-01, Vol.46 (3), p.846-853

2007

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Economically efficient operation of CO2 capturing process part I: Self-optimizing procedure for selecting the best controlled variables

Chemical engineering and processing, 2011-03, Vol.50 (3), p.247-253

2011

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Nonlinear control solutions to prevent slugging flow in offshore oil production

Journal of process control, 2017-06, Vol.54, p.138-151

2017

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Feedback Real-Time Optimization Strategy Using a Novel Steady-state Gradient Estimate and Transient Measurements

Industrial & engineering chemistry research, 2019-01, Vol.58 (1), p.207-216

2019

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On combining self-optimizing control and extremum-seeking control – Applied to an ammonia reactor case study

Journal of process control, 2019-06, Vol.78, p.78-87

2019

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Plantwide control: the search for the self-optimizing control structure

Journal of process control, 2000, Vol.10 (5), p.487-507

Skogestad, Sigurd

2000

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Optimal operation of oil and gas production using simple feedback control structures

Control engineering practice, 2019-10, Vol.91, p.104107, Article 104107

2019

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Control structure design of a solid oxide fuel cell and molten carbonate fuel cell integrated system: Bottom-up analysis

Energy conversion and management, 2020-09, Vol.220, p.113021, Article 113021

2020

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Optimal PI and PID control of first-order plus delay processes and evaluation of the original and improved SIMC rules

Advanced control using decomposition and simple elements

Real-Time Optimization under Uncertainty Applied to a Gas Lifted Well Network

Comparison of Model-Based Control Solutions for Severe Riser-Induced Slugs

Primal–dual feedback-optimizing control with override for real-time optimization

Simple analytic rules for model reduction and PID controller tuning

A sensory-motor control model of animal flight explains why bats fly differently in light versus dark

A Rigorous Model for Evaluating Moving Window Soft Sensors for Industrial Distillation Processes

Online Process Optimization with Active Constraint Set Changes using Simple Control Structures

Simple analytic rules for model reduction and PID controller tuning

Linear parameter-varying model for a refuellable zinc-air battery

Dividing Wall Columns for Natural Gas Liquefaction Plants

The setpoint overshoot method: A simple and fast closed-loop approach for PID tuning

The use of first principles model for evaluation of adaptive soft sensor for multicomponent distillation unit

Optimization of fixed-order controllers using exact gradients

Tuning for Smooth PID Control with Acceptable Disturbance Rejection

Economically efficient operation of CO2 capturing process. Part II. Design of control layer

Null Space Method for Selecting Optimal Measurement Combinations as Controlled Variables

Economically efficient operation of CO2 capturing process part I: Self-optimizing procedure for selecting the best controlled variables

Nonlinear control solutions to prevent slugging flow in offshore oil production

Feedback Real-Time Optimization Strategy Using a Novel Steady-state Gradient Estimate and Transient Measurements

On combining self-optimizing control and extremum-seeking control – Applied to an ammonia reactor case study

Plantwide control: the search for the self-optimizing control structure

Optimal operation of oil and gas production using simple feedback control structures

Control structure design of a solid oxide fuel cell and molten carbonate fuel cell integrated system: Bottom-up analysis

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