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Here we extend the previous model by including energy balance equations and a Generic Model Control algorithm to simulate the temperature controller of the crystallizer that tracks a cooling profile during crystallization.
The controller is composed of giant magnetostrictive actuator model control algorithm and PID control algorithm to realize the output force reaching the constant force target quickly and finely adjustment.
A prediction based interval model control algorithm was then implemented to control the resultant interval models.
Experimental results confirmed the effectiveness of the adaptive interval model control algorithm and the developed control system.
This scheme combines the generic model control algorithm (GMC), with a nonlinear observer, which is able to track changes in the process model.
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Building on a passivity-based control, we develop decentralized two-time-scale adaptive internal model control algorithms that estimate the unknown frequency information and achieve consensus of the group.
The paper outlines issues regarding compressor design, compressor string modeling, control algorithms, controller performance, and surge protection.
The score predictive model for the model predictive control algorithm is built using neural network partial least squares (NNPLS).
Insulin dosing was controlled by a customized model predictive control algorithm incorporating a pharmacokinetic model for insulin lispro that assumed a t max of 65 min. Glucagon dosing was controlled by a customized proportional derivative algorithm.
In this paper, we develop a finite horizon model predictive control algorithm which is robust to model uncertainties.
Another main type of glucose algorithm used in critical care is the model-based or model-predictive control algorithm, which adjusts insulin dose according to a mathematical model of the relationship between blood glucose and insulin [ 54, 55].
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