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This paper presents a neural network-based control method for achieving desired lighting levels in an LED-based lighting system with unknown or uncertain system model parameters in the presence of daylight disturbances. Assuming an unknown system model matrix, the control strategy utilizes an online neural network method to synthesize a learning controller. The control commands are dimming levels,...
This paper presents a simulation environment and daylighting control strategy to achieve energy-efficient lighting while providing desired lighting levels at the target points. The lighting strategy is based on a self-tuning multivariable controller, which maintains the illuminance levels at user-defined set-points while improving the energy consumption due to artificial lighting. The simulation environment...
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