A survey of industrial model predictive control technology

Qin, S. Joe; Badgwell, Thomas A.

doi:10.1016/s0967-0661(02)00186-7

Cited by 4,424 publications

(2,557 citation statements)

References 32 publications

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“…For adaptive interventions involving multiple outcomes (such as those associated with co-morbidities) and multicomponent interventions, the concept of Model Predictive Control (MPC) (Prett and García, 1988;Camacho and Bordons, 1999;Qin and Badgwell, 2003) seems promising. As a multivariable control design technique that uses optimization methods to make control decisions in real-time, MPC can serve as the basis for decision rules that involve multiple components and address multiple outcomes simultaneously while satisfying explicit problem constraints.…”

Section: Decision Rules Based On Engineering Control Principlesmentioning

confidence: 99%

Using engineering control principles to inform the design of adaptive interventions: A conceptual introduction

Rivera

Pew

Collins

2007

Drug and Alcohol Dependence

134

136

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The goal of this paper is to describe the role that control engineering principles can play in developing and improving the efficacy of adaptive, time-varying interventions. It is demonstrated that adaptive interventions constitute a form of feedback control system in the context of behavioral health. Consequently, drawing from ideas in control engineering has the potential to significantly inform the analysis, design, and implementation of adaptive interventions, leading to improved adherence, better management of limited resources, a reduction of negative effects, and overall more effective interventions. This article illustrates how to express an adaptive intervention in control engineering terms, and how to use this framework in a computer simulation to investigate the anticipated impact of intervention design choices on efficacy. The potential benefits of operationalizing decision rules based on control engineering principles are particularly significant for adaptive interventions that involve multiple components or address co-morbidities, situations that pose significant challenges to conventional clinical practice.

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Section: Decision Rules Based On Engineering Control Principlesmentioning

confidence: 99%

Using engineering control principles to inform the design of adaptive interventions: A conceptual introduction

Rivera

Pew

Collins

2007

Drug and Alcohol Dependence

134

136

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“…Control systems are usually composed of a set of networked agents, consisting of sensors, actuators, control processing units such as programmable logic controllers (PLCs), and communication devices. Modern day industrial control systems have a multi-layer structure [11]. The overall objectives of such a control structure are: (1) to maintain safe operational goals by limiting the probability of undesirable behavior, (2) to meet the production demands by keeping certain process values within prescribed limits, (3) to maximize production profit.…”

Section: Introductionmentioning

confidence: 99%

A New Method Against Attacks on Networked Industrial Control Systems

Truong¹,

Tu²

2017

Fair - Nghiên Cứu Cơ Bản Và Ứng Dụng Công Nghệ Thông Tin - 2016

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Section: Introductionmentioning

confidence: 99%

“…MPC can be explicitly handled in a united linear or nonlinear programming formula and is first applied to control in oil refining processes (Qin and Badgwell, 1997). At present, as MPC has the strong robustness and can significantly reduce the variance of the control process output (Qin and Badgwell, 2003), it has been widely applied to many industries, including petroleum, chemical industry, paper making, food processing, and aviation. The classic algorithm of MPC mainly includes the dynamic matrix control (DMC) (Cutler and Ramaker, 1980), the model algorithm control (MAC) (Richalet et al, 1978), and the generalised predictive control (GPC) (Clarke et al, 1987).…”

Section: Introductionmentioning

confidence: 99%

Analysis and Correction of Ill-Conditioned Model in Multivariable Model Predictive Control

Peng

Huang

Zou

et al. 2015

Intelligent Robotics and Applications

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Ill-conditioned model usually appears in modelling high purity and complicated production processes such as the high-purity distillation column. Model predictive control (MPC) via closed-loop feedback is a class of methods for which a plant model is used to forecast the outputs or states of controlled systems and to then solve a linear or nonlinear programming with multivariable and constraints by so-called receding horizon optimisation. Ill-conditions can cause many negative effects on the implementation of MPC including system instability and controller failure. In this paper, these phenomena such as output static error and stability decreasing of ill-conditioned model caused in MPC are found by simulating simple examples, and the close correlation between the movement direction of the controlled system output and the characteristics of ill-conditioned model is also observed. The geometry tools and singular value decomposition (SVD) in linear algebra are used to analyse the essential cause of ill-conditioned model generation. A new offline quantitative strategy is proposed to improve the ill-conditioned model. Based on modified model of reengineering and implementation of model predictive control, the closed-loop control performance and stability can be significantly enhanced.

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A survey of industrial model predictive control technology

Cited by 4,424 publications

References 32 publications

Using engineering control principles to inform the design of adaptive interventions: A conceptual introduction

Using engineering control principles to inform the design of adaptive interventions: A conceptual introduction

A New Method Against Attacks on Networked Industrial Control Systems

Analysis and Correction of Ill-Conditioned Model in Multivariable Model Predictive Control

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