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A Neural-Network-Based Color Control Method for Multi-Color LED Systems

2018; Institute of Electrical and Electronics Engineers; Volume: 34; Issue: 8 Linguagem: Inglês

10.1109/tpel.2018.2880876

1941-0107

Xiaoqing Zhan, Wenguan Wang, Henry Shu-Hung Chung,

Optical Wireless Communication Technologies

A neural-network-based color control method for multi-color light-emitting-diode (LED) systems is proposed. The proposed control method can achieve color control with high color rendering indexes even when there is ambient light. The spectral peak control provides data points with high color rendering indexes. These data points would be updated and utilized for the training of neural networks on microcontroller unit. The trained neural networks can achieve the color control of multi-color LED systems with or without ambient light. Compared with previous color control methods, the proposed method achieves color control with high color rendering index with or without ambient light, does not require an accurate electrical-optical system model, is applicable to all multi-color LED systems, and improves the performance without modifying any hardware. The proposed color control method is tested on an application example of a red-green-blue-amber (RGBA) LED system. The RGBA LED channels are connected in series and driven by a buck-type single-inductor-multiple-output LED driver. The effectiveness and robustness of the proposed control method under different types of ambient lights have been verified experimentally.