Reinforcement learning control of multi-spectral LED systems for rendering optimal lighting performance in indoor environment

Abstract

Light-emitting diodes (LEDs) have superseded traditional light sources due to their superior qualities. However, achieving desired spectral distribution in indoor environment still remains a challenge, particularly for optimizing multiple lighting parameters, which are nonlinear functions of the light spectra, including correlated color temperature (CCT), color rendering index (CRI) and circadian action factor (CAF). Existing studies have been mainly devoted to seeking optimal solutions, by means of various optimization methods, e.g. genetic algorithms. However, these methods can only result in fixed recipes to achieve pre-assigned reference parameters, and cannot meet the need of dynamic varying lighting environment. Therefore, this paper proposes a novel framework for optimal spectrum design and control in multichannel LED lighting systems. It surpasses traditional methods by employing reinforcement learning (RL) to generate an entire optimal spectral power distribution (SPD) model, comprehensively considering CCT, CRI, CAF, and other relevant parameters. This framework integrates the RL-based optimal SPD generator with a corresponding real-time controller, bridging the gap between simulations and real-world applications. The effectiveness of this new framework has been verified by an experimental prototype, showcasing both the optimal SPD and the achieved control performance. This work paves the way for high-quality, user-centric lighting with dynamic CCT and spectral control, and enables the creation of lighting environments that optimize human well-being, productivity, and overall experience.