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The controller contains three 1.5-volt AA batteries and produces only about 16 milliamps of current.
Following this approach, the designed controller contains only one actual control law and one adaptive law.
The proposed controller contains two parts: tracking controller and friction compensator.
This multi-model fuzzy controller contains four local fuzzy controllers which correspond to four velocity intervals.
The designed controller contains a PI component and a lag compensator for dynamic correction at high frequencies.
In its first layer the controller contains a recently introduced fuzzy algorithm for the solution of the inverse kinematics for redundant robots.
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The temperature of the recording chamber was carefully controlled (±0.1°C) using a home-made temperature controller containing a Peltier device fixed to the microscope stage.
A procedure is given for designing the proposed periodic controller (containing an internal model of the reference signals).
The RCST periodically receives terminal burst time plan (TBTP) messages sent by the DAMA controller, containing information on assigned timeslot resources for the next super-frame (SF) period.
The computation controller containing a self-structuring fuzzy neural network (SFNN) identifier is the principle controller, and the robust controller is designed to achieve L2 tracking performance.
The computation controller containing a DSCN identifier is the principal controller and the robust compensator is designed to achieve L2 tracking performance with a desired attenuation level.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com