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This controller provides an optimal tracking of the predicted control target.
A remaining challenge of this approach is that the controller has uncertain information about the actual control inputs, which leads to the predicted control input errors.
This theoretically predicted control system performance is illustrated via simulation experiments, which also show that the properties hold under parameter uncertainties.
A single step predicted control algorithm is integrated with the priority matrix into a coordinated control system which is easy to understand and is comparable in performance with dynamic matrix control (DMC).
We show how to design MDs for packetized predicted control (PPC) in order to enhance the robustness.
In order to compare the actual and predicted control areas, the division of control area of this model is shown in Fig. 8.
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Forstmann, B. U. et al. Cortico-striatal connections predict control over speed and accuracy in perceptual decision making.
To better manage the inherent risks, we need to understand, model, design, predict, control, and optimize the dynamics and behavior of such systems.
In other word, predict control ensures the system always operating inside the DSSR.
Finally, if the operating point is predicted to overflow the DSSR in the future, predict control will be implemented to correct its development trend.
Gisario et al. (2011) used neural network solutions to predict, control and manage the spring-back in laser assisted V-shape bending of thin aluminum sheets.
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