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The experimental results present good accuracy and performance of the control algorithm.
The performance of the control algorithm was verified through real-world vehicle tests, resulting in an approximately 30% improvement in fuel economy, compared to the conventional excavator.
The performance of the control algorithm is evaluated via numerical simulations in the face of load disturbances, parameter kinetic uncertainties and set-point changes.
The robust performance of the control algorithm is intensively assessed in silico on a cohort of virtual patients under challenging realistic scenarios considering mixed meals, circadian variations, time-varying uncertainties, discrete measurement and actuation, sensor errors and other disturbances.
In order to analyze the performance of the control algorithm, extensive closed-loop simulations are illustrated using a detailed nonlinear model of the subcutaneous insulin-glucose dynamics in a TIDM patient with meal intake.
In this paper the neural network approach has been applied to Duplex/Triplex group control systems for improving passenger waiting time and a lift simulation software has been developed and implemented in order to assess the learning capability by measuring the performance of the control algorithm.
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The performance of the control algorithms are illustrated by simulations performed for a recycled wastewater treatment bioprocess for which kinetic dynamics are highly nonlinear, time-varying and incompletely known.
Finally, two types of trajectories are designed to validate the performances of the control algorithms, and experiments are performed on the excavator trajectory control experimental platform.
In this study, we compare the performance of this control algorithm with the patient's usual pump setting.
Disturbances from several sources cause a deterioration in control performance, and thus a major role of the control algorithm is effective disturbance rejection.
The RTBMPC design with the pre-terminal set aids in determining the trade-off between the complexity of the control algorithm with the performance of the closed-loop system while ensuring robust stability.
More suggestions(15)
performance of the control input
performance of the control structure
performance of the control room
performance of the control design
performance of the control scheme
performance of the control approach
performance of the control method
performance of the control chart
performance of the control configuration
performance of the control strategy
performance of the control mixture
performance of the control rod
performance of the summation algorithm
performance of the search algorithm
performance of the control technique
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