Data-driven modeling of the thermal dynamics of a dual-spectral ultraviolet sterilizer and control of the output irradiance

Multi-channel LED light sources possess complicated nonlinear photo-electro-thermal (PET) dynamics. Modeling such PET dynamics is challenging, but important to ensure the performance of these light sources. Traditional first-principle modeling methods that have been reported in the literature yield either singlechannel LED dynamic models, or multi-channel static models. On the other hand, system identification (SID) methods have also been applied, and lead to both single-channel nonlinear dynamic models and multi-channel linear time invariant (LTI) models. However, there are still few attempts given to build multiple-input-multipleoutput (MIMO) nonlinear dynamic models of multi-channel LED systems. In this work, we try to model the PET dynamics of a dual-spectral ultraviolet (UV) light source, with a structured nonlinear system identification technique, i.e., linear parameter varying (LPV) SID technique. The experimental prototype contains a 280 nm UVC channel and a 365 nm UVA channel, respectively driven by two separate constant current sources, and is hence a MIMO system. Its nonlinear electro-thermal model is first identified from experimental data by the LPVSID method, which is then integrated with the estimated photo-electro model to form a complete PET dynamic model. The experimental results have verified that the model can accurately predict the temperature variations at two representative points on the LED board. Moreover, the identified model has been applied in designing a feedforward-feedback controller to precisely track the reference radiation levels of the two UV channels, despite the strong nonlinearity caused by both the electro-thermal dynamics and the temperature-dependent lumen efficacy

Automated summary

This paper attempts to model the photo-electro-thermal dynamics of a dual-spectral UV light source using a structured nonlinear system identification technique. The nonlinear electro-thermal model is identified from experimental data and integrated with the photo-electro model to form a complete dynamic model. Experimental results verify the accuracy of the model in predicting temperature variations and its application in controller design.