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Experimental results of the proposed BBSA schedule controller are compared with the binary particle swarm optimization (BPSO) schedule controller to verify the accuracy of the developed controller in the HEMS.
The BBSA schedule controller provides better results compared to that of the BPSO schedule controller in reducing the energy consumption and the total electricity bill and save the energy at peak hours of certain loads.
In the present study, real time optimal schedule controller for HEMS is proposed using a new binary backtracking search algorithm (BBSA) to manage the energy consumption.
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A scheduling controller adapts control task periods to regulate the processor load.
In the system, an intelligent method based on hybrid neural networks is designed for state estimation and fault diagnosis, and a gain scheduling controller is adapted for system control.
By combining the parametric Riccati equation and the existing gain scheduling technique, a new robust gain scheduling controller is proposed to solve the robust control problem.
This structure is based on a Model Reference Adaptive Control (MRAC) with a H∞ Gain Scheduling controller designed using an Linear Parameter- Varying (LPV) system: MRAC-H∞ GS-LPV.
Gain scheduling controller design scheme employing piecewise affine (PWA) model for nonlinear controlled plant is given in the paper.
We show further that the gain scheduling controller yields best performance.
Both pilots consistently awarded Level 1 handling qualities ratings for the LPV gain-scheduled controller.
Moreover, based on sector narrowing techniques for the saturation function, a nonlinear scheduled controller is proposed.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com