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Based on the variable structure control (VSC) theory, the sliding mode control scheme is used to design a chaos suppression controller in this study.
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The LQR methodology is extended to produce multiloop PI controllers in this study.
The authors thank all patients and healthy controllers in this study.
*) As the number of elite controllers in this study is only 3, all three single values are depicted instead of the median.
The first controller developed in this study is an adaptive fuzzy neural controller (AFNC).
The simulation results show that the FGPI controller developed in this study performs better than the rest of the controllers on the settling time and overshoot of power and enthalpy outputs.
The controller presented in this study ensures safety in relation to velocity and stability by imposing time-invariant constraints on the controller input and body tilt angle.
The controller used in this study shows a Dead-Beat-behavior.
The robot motion controller used in this study and described in Section 4 exploits this concept in a simple way by just keeping the natural Cartesian inertia of the robot, a stiffness monotonically increasing with time and a damping dependent upon the stiffness.
The controller proposed in this study involves the switching of not only the traveling direction but also a parameter value, which is allowed to switch to an arbitrary value at an arbitrary time while theoretically maintaining the stability of the system.
Neural Network (NN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are two intelligent controllers implemented in this study.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com