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The proposed behaviour based controller is able to outperform all its opponents in direct competition, and is also the most computationally efficient.
The experiments on a spark ignition gasoline engine at various operating conditions show that the hypothesis test based controller is able to regulate LPP close to set point while maintaining the rapid transient response, and the variance of LPP is also well constrained.
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Using Table 1, the knowledge-based controller was able to stabilize and regulate the SaO2 value.
Secondly, we leverage the GP-based prediction mechanism for the CQI assignment problem, in which a base station controller is able to monitor the behaviour of each served user and assign a personalised prediction time window based on that user's performance and requirements.
The designed simplified fuzzy based sliding surface controller is able to balance the ball and beam system around its equilibrium state, which as a control perspective shows that performance of this controller is better than the conventional SMC.
A single controller is able to control both technologies by using multiple interfaces.
And our controller is able to generate continuous reaching motion based on virtual character׳s previous status with no need to start from one same initial pose.
The dual controller is able to monitor and predict the base station's performance and assign a time window to each user based on specific requirements.
The coordination controller is able to ensure the respect of coordination policy.
The controller is able accurately to detect the movement of 10 fingers at once, allowing for intricate gesture-based controls.
A specific FiO2 controller is able to maintain SpO2 reliably within a predefined target range.
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