Research On Nonlinear Modeling And Dimming Control Algorithm Of High-power LED System Based On PET Theory

In recent years,LEDs have developed towards higher power density and integration,providing potential solutions for interactive or dynamic lighting: such as multi-spectral LED solar simulators,hyper-spectral crop cultivation lights,surgical medical shadowless lights,and buildings lighting rendering,etc.Such applications usually require that the optical output of the system be continuously adjustable and reach a predetermined luminosity,chromaticity,etc.within a limited time.With the diversification of application requirements,the realization of fine dimming control is the development trend of the LED field.Although LEDs have high luminous efficiency and longer lifespan,due to their light-emitting mechanism,LED systems inevitably suffer from heating problems.Due to the interaction of light-electricity-heat(PET)multi-physical domains when LEDs work,the thermodynamic process of the system includes time lag,time-varying and other nonlinear characteristics,and the system is susceptible to uncertain interference such as environmental temperature and device performance degradation,it is difficult to predict the change law of luminosity output and fine dimming control,especially in high-power LED systems.General lighting mainly focuses on improving the light efficiency of the system,reducing energy consumption,and controlling production costs.In practical applications,it only works in a few operating points,which does not require high optical accuracy,and often uses linear model-based control methods,which makes it difficult to apply tunable light sources with fine control requirements.At the same time,due to the low level of automation of these systems,it often leads to excessive consumption of energy.In order to achieve the application goal of fine control of high-power LED tunable light source,this paper will use PET theory as a guide to carry out research on the photoelectric and thermal characteristics analysis,modeling and dimming control strategy of high-power LED systems.The specific research content is as follows:(1)The light-electricity-thermal theory is an important theory describing the coupling relationship among LED optical,electrical and thermal parameters.Starting from the analysis of the LED mechanism,this article uses a wide range of resistance-capacitance network modeling methods to derive and construct a parametric photoelectric thermal model,and perform qualitative and quantitative calculations and analysis on the photoelectric thermal characteristics of the system,so as to provide a theoretical basis for subsequent modeling and control methods.(2)The operation of the LED system has a light-electricity-heat multi-parameter coupling relationship,and is affected by uncertain factors such as ambient temperature and device aging.In order to solve the mechanism modeling method,the test process is cumbersome,the model is complex,and it is difficult to directly apply it.Due to limitations such as weak adaptability,data-driven fuzzy modeling technology research has been carried out.On the existing basis,an online modeling algorithm based on self-organizing fuzzy neural network is designed.This method integrates clustering,error information and error reduction rate analysis to realize automatic rule design,and uses recursive square method and extended Kalman filter The algorithm realizes the parameter update.The proposed method is verified on the simulation experiment of typical nonlinear dynamic system and Mackey-Glass chaotic time series.The results show that compared with other typical models that are widely used,the fuzzy model constructed by the algorithm in this paper has a good balance between structural complexity and prediction accuracy,and has high computational efficiency.(3)Combined with the above-mentioned modeling algorithm,a photoelectric thermal model based on self-organizing fuzzy neural network is constructed to provide a model basis for subsequent predictive control.In addition to considering the influence of drive current and heat sink temperature on luminosity output,this model also takes into account the input interference of ambient temperature,which can accurately predict the change of luminous flux output with changes in operating temperature.Aiming at the undesirable non-linear phenomena such as luminous flux attenuation,overshoot and hysteresis caused by temperature,a refined dimming algorithm based on self-organizing fuzzy neural network predictive control is designed,and the stability of the control system is proved by Lyapunov theory,and its It is applied to the dynamic tracking control of different luminous flux trajectories.In the experimental part,constant value control,tracking control and robustness tests to different ambient temperatures are designed respectively.The results show that compared with conventional control methods,the dimming control method proposed in this paper has a significant improvement in speed,stability and accuracy.It can not only overcome the optical hysteresis caused by temperature,but also affect the ambient temperature.Better robustness.This paper designs a data-driven fine dimming control scheme.In addition to being applied to luminosity tracking,it can also be appropriately expanded according to specific targets,such as introducing spectrum and chromaticity.It is not only suitable for the LED field,but also provides technical reference support in other lighting systems.