Generalized predictive control—Part I. The basic algorithm

Clarke, D.W.; Mohtadi, C.; Tuffs, P.S.

doi:10.1016/0005-1098(87)90087-2

Cited by 3,493 publications

(1,117 citation statements)

References 5 publications

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“…These algorithms include the Model Algorithmic Control (MAC) [12], the Dynamic Matrix Control (DMC) [3] and the Generalized Predictive Control (GPC) [2]. These algorithms are all similar in the sense that they rely on process models to predict the behavior of the process over some future time interval, and the control calculations are based on these model predictions.…”

Section: B Linear Model Predictive Controlmentioning

confidence: 99%

“…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. Several control applications can be labeled as safety-critical: their failure can cause irreparable harm to the physical system being controlled and to the people who depend on it.…”

Section: Introductionmentioning

confidence: 99%

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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: B Linear Model Predictive Controlmentioning

confidence: 99%

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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“…The control strategies presented here are based on one of the most popular algorithms: Generalized Predictive Control (GPC) [5]. The output prediction in GPC is given by a CARIMA (Controlled Auto-Regressive Integrated Moving Average) model of the plant:…”

Section: Model Based Predictive Controlmentioning

confidence: 99%

“…All these issues make the plant be a good testbed for the illustration of the capabilities of the nonlinear predictive control strategies based on fuzzy models quoted before. Two different techniques have been developed using neurofuzzy models with Generalized Predictive Control (GPC) [5] and applied to control the thermal sterilization process in steam retorts. This paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

Neurofuzzy model based predictive control for thermal batch processes

Escaño

Bordons

Vilas

et al. 2009

Journal of Process Control

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In many cases, it is difficult to derive a precise mathematical model, based on first principles, for a given process. Besides, the computation of the solution of models obtained through this methology may require a large computational effort making them useless for real time tasks like control or optimization. Neurofuzzy modelling, which permits an easy way to derive successful models, is a good alternative which can be employed to overcome such limitations. In this paper, together with the neurofuzzy modelling, several strategies based on non-linear predictive control are presented. The low computational cost associated with neurofuzzy models and controllers makes them suitable candidates to be implemented into industrial Programmable Logic Controllers (PLC). Both the model and controllers are validated and implemented in a pilot plant for the thermal sterilization of solid canned food in steam retorts and based on the results, a comparison between the different predictive control strategies is presented.

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“…A standard sensor interface as (Pertin & Bonnet des Tuves, 2004) q . For curved paths to be executed at high speed we recommend to increase accuracy using advanced control methods which process not only the current desired pose but also the desired speed and acceleration or -what we use -the desired poses of multiple future sampling steps as (Clarke et al, 1987) or (Grotjahn & Heimann, 2002). Fig.…”

Section: Position Controlmentioning

confidence: 99%