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Therefore, it is difficult to operate at an accurate trajectory using conventional controllers and a feed-forward neural network is put forward to resolve it [19 21].
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However most of HVAC systems are controlled by using conventional controller whose functions are based on ON/OFF controller and Proportional-Integral-Derivative (PID) controllers.
This study describes a global approach of controlling chaos to reduce tedious waiting time caused by using conventional local controllers.
Such processes typically have complex and nonlinear dynamic behavior, which causes the performance of controllers using conventional design and tuning to be poor or to require significant effort in practice.
The current control loop uses conventional PI controller and its output is given to the inverter as demanded voltage for switching.
Meal corrections were given by the controller based on the glycemic state, with postprandial delivery equaling 0.2 1.4 times the insulin expected if using conventional therapy, although it is recognized that CFs and insulin-to-carbohydrate ratios are only estimates and may result in either under- or overdelivery.
Then, the controller uses conventional Proportional-Integral-Derivative control theory, and a robust two-degree-of-freedom controller is also carried out to determine the optimal control parameters to further improve the system performance.
The rigorous review indicates that existing optimization algorithms in conventional controller and FLC can be used for IM drive.
It is difficult to overcome the effects of these factors and get a reasonable results with the use of conventional controllers such as PID.
Therefore, in spite of their ability to generalize, the learning controllers are usually used as subordinates to conventional controllers or the learning process needs to be repeated each time a new trajectory is encountered.
The simulation study is made on the conical tank level process and the results are compared with those obtained using a conventional PI controller and the Smith PI controller based on the transfer function model about the operating point 39%.
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