A nonlinear DMC algorithm and its application to a semibatch polymerization reactor

Peterson, T.J.; Hernández, Evelio; Arkun, Yaman; Schork, F. Joseph

doi:10.1016/0009-2509(92)80265-e

Cited by 110 publications

(56 citation statements)

References 5 publications

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“…The algorithm for systems with no constraint is introduced by Peterson et al (1992) and by Draeger et al (1995). A brief description of this method is presented in the following.…”

Section: Non-linear Dmcmentioning

confidence: 99%

Extended Linear QDMC of a Gibbsite Crystalliser: A Simulation Study

2003

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“…The algorithm for systems with no constraint is introduced by Peterson et al (1992) and by Draeger et al (1995). A brief description of this method is presented in the following.…”

Section: Non-linear Dmcmentioning

confidence: 99%

Extended Linear QDMC of a Gibbsite Crystalliser: A Simulation Study

2003

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“…It is now widely recognized as a powerful methodology to address industrially important control problems. The general strategy of an MPC algorithm is to utilize a model to predict future outputs of the process and minimize the difference between the predicted and desired outputs in order to compute the appropriate control action (Peterson et al, 1992). Two major types of MPC are dynamic matrix control (DMC) and model algorithmic conlrol (MAC).…”

Section: Model Predictive Controlmentioning

confidence: 99%

MODELING AND NONLINEAR CONTROL OF A CONTINUOUS KCI-NaCI CRYSTALLIZER

Tadayyon

Rohani

2000

Particulate Science and Technology

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A controller is developed by combining the extended linear quadratic matrix control (EQDMC) and neural network algorithms. The dynamic neural network scheme is used to identify the process and generate a nonlinear model. The control algorithm is applied to a multi-input multi-output (MIMO) evaporative cooling KCI-NaCI-Hz0 crystallizer. Closed loop responses of the system using the proposed algorithm and those of PID controllers are compared. It is shown that in all cases, the response of the proposed controller to step changes in setpoints is faster than the PID controllers

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“…Both NLMPC algorithms are based on the full nonlinear model, that is, the ypast and S(') for both algorithms have been calculated from the nonlinear model. The step-response matrix S(l) at each sam- pling interval was calculated by the method described in Peterson et al (1992). The S(') matrix at each sampling interval was determined using a forward difference type of approximation (see Mutha et al, 1997b, Eq.…”

Section: Multivariable Controller Peflomncementioning

confidence: 99%