Model algorithmic control of industrial processes

Richalet, J.; Rault, A.; Testud, J. L.; Papon, J.

doi:10.1016/s1474-6670(17)69513-2

Cited by 68 publications

(54 citation statements)

References 5 publications

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“…Early development (Richalet et al (1976), Richalet et al (1978), Cutler & Ramaker (1980)) of the control approach known today as Model Predictive Control (MPC) originated in the process control community, and was driven much more by industrial application than by theoretical understanding. Modern theoretical understanding of MPC, much of which developed throughout the 1990's, has clarified its very natural ties to existing optimal control theory.…”

Section: Model Predictive Control As Receding-horizon Optimizationmentioning

confidence: 99%

Robust Adaptive Model Predictive Control of Nonlinear Systems

DeHaan¹,

Guay²

2010

Model Predictive Control

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Section: Model Predictive Control As Receding-horizon Optimizationmentioning

confidence: 99%

Robust Adaptive Model Predictive Control of Nonlinear Systems

DeHaan¹,

Guay²

2010

Model Predictive Control

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“…multivariable control (Richalet, Rault, Testud, & Papon, 1976, 1978. One reason for its success is its ability to handle multivariable systems subject to input and output constraints (Dochain, Marquardt, Won, Malik, & Kinnaert, 2006).…”

Section: Process Control and Mpcmentioning

confidence: 99%

“…6 as an evolutionary tree for the most significant industrial MPC algorithms (Qin & Badgwell, 2006). The first description of MPC control application was presented by Richalet et al (1976Richalet et al ( , 1978, and the developed software was called IDCOM. The dynamic matrix control (DMC) algorithm also represented the first generation of MPC technology and was developed by Shell Oil engineers.…”

Section: Process Control and Mpcmentioning

confidence: 99%

Future trends in process automation

Jämsä‐Jounela¹

2007

Annual Reviews in Control

110

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The importance of automation in the process industries has increased dramatically in recent years. In the highly industrialized countries, process automation serves to enhance product quality, master the whole range of products, improve process safety and plant availability, efficiently utilize resources and lower emissions. In the rapidly developing countries, mass production is the main motivation for applying process automation. The greatest demand for process automation is in the chemical industry, power generating industry, and petrochemical industry; the fastest growing demand for hardware, standard software and services of process automation is in the pharmaceutical industry.The importance of automation technology continues to increase in the process industries. The traditional barriers between information, communication and automation technology are, in the operational context, gradually disappearing. The latest technologies, including wireless networks, fieldbus systems and asset management systems, boost the efficiency of process systems. New application fields like biotechnology and microtechnology pose challenges for future theoretical work in the modeling, analysis and design of control systems. In this paper the industry trends that are shaping current automation requirements, as well as the future trends in process automation, are presented and discussed. #

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“…Chapter 3 gives an overview of some MPC algorithms widely used in industry, like dynamic matrix control (DMC) [2], model algorithm control (MAC) [3] and predictive functional control (PFC) [12]. Again in this chapter, only an introduction of the mentioned controllers is given, trying to point the main characteristics and the reasons for the success in real applications.…”

Section: The Chaptersmentioning

confidence: 99%

“…Perhaps the main reasons for this success are that: it can be applied to multivariable systems, the feedback and feed-forward actions can be included in a straightforward manner, it intrinsically compensates for the dead-times and, furthermore, the input and output constraints can be included in the formulation of the control law through on-line optimization. Several MPC techniques, such as dynamic matrix control (DMC) [2] and model algorithm control (MAC) [3], have demonstrated their effectiveness in industrial applications. These algorithms are based on step or impulse response models of the plant and the disturbances are considered as the difference between the real output and the predicted one.…”

Section: Introductionmentioning

confidence: 99%

Model predictive control springer, Berlin, 1999, ISBN 3540762418, 280 pages

Camacho

Bordons

Normey‐Rico

2003

Intl J Robust & Nonlinear

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BOOK REVIEWModel predictive control Springer, Berlin, 1999, ISBN 3540762418, 280 pages SUMMARY This volume is a recent addition to the Camacho and Bordons' book 'Model Predictive Control in the Process Industry', edited by Springer Verlag. The book presents a complete review of the theory and applications of Model Predictive Control MPC, from the simple unconstrained SISO case to the more complex constrained MIMO situations. Special attention is given to the Generalized Predictive Controller that is one of the most known and cited MPC strategies. In all the chapters the results are illustrated with simulation examples and also with some experimental results that validated the controllers and tuning rules analyzed in the book.

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Model algorithmic control of industrial processes

Cited by 68 publications

References 5 publications

Robust Adaptive Model Predictive Control of Nonlinear Systems

Robust Adaptive Model Predictive Control of Nonlinear Systems

Future trends in process automation

Model predictive control springer, Berlin, 1999, ISBN 3540762418, 280 pages

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