Sentence examples for models for control from inspiring English sources

The paper1 is focused on system identification in closed loop as a tool for obtaining improved models for control design and reduced order controllers.

Unlike other existing dynamic CCU models for control and optimization, this modeling method is relatively simple and exactly captures the nonlinear characteristics over a wide operating range of the CCU without requiring geometric specifications, which is very convenient in real time engineering control practice.

The proposed approach has a double functionality; on the one hand, it allows a better performance of using nonlinear models for control purposes, like for nonlinear predictive controllers; and on the other hand, it can be used as a diagnosis mechanism since it provides relevant information about the current state of the system.

For such models, powerful control synthesis tools are available, but the way how to systematically convert available first principles models to LPV descriptions of the plant, to efficiently identify LPV models for control from data and to understand how modeling errors affect the control performance are still subject of undergoing research.

Physical modeling can provide qualitative results but fails to yield enough accurate models for control design.

Use of the suggested fuzzy models for control of fuel air relationship and boiler water level are described as examples.

This approach can provide reliable models for controlling of such systems.

The results of its use in uveitis in clinical practice and animal models for controlling inflammation and improving CME and visual acuity have been encouraging [4, 5].

As animals present good models for controlled experimentation, we chose domestic horses to investigate this issue.

We assessed the utility of six statistical models for controlling type I and II errors in this B. napus panel.

Authors have included heuristic algorithms in their simulation model for control determining of the assembly line.