多光谱LED光源已被用于机器视觉、医学诊疗等领域。然而，由于这种光源具有多变量、非线性的光电热动力学特性，现有技术中尚缺乏对其有效地建模及控制其输出光的方法。本项目针对这种系统的物理特性，研究线性变参数（LPV）状态空间系统辨识和离散时间LPV鲁棒与分布式控制技术，以获取该系统的模型和反馈控制器。该方法可有效解决系统中存在的多变量、非线性的问题，实现在温度大幅波动时保持输出光参数及光谱稳定的目标。另外，现有的多光谱成像技术尚未解决当被照物体的表面光谱反射率未知时，如何优化图像对比度的问题。本项目将研究迭代学习控制技术以解决这一难题，提出数据驱动的光谱优化方法，并研究上述控制技术在生物组织的多光谱成像中的应用。本项目提出的系统辨识与迭代学习控制方法将分别解决基于第一原理不易对多光谱LED光源建模、及无法获取物体表面光谱反射率的问题，为这种光源在多光谱成像、医学诊疗等领域中的应用奠定科学基础。

The application of multi-spectral LED light sources has been extended to high-end applications, such as machine vision, medical diagnosis and therapies. However, due the complex nonlinear photoelectrothermal (PET) behavior of multi-spectral LED light sources, there still lack an efficient modeling technique and the control method that can precisely regulate their brightness, chromaticity and spectra. In this project, we will develop and apply new linear-parameter varying (LPV) system identification and robust control techniques for discrete time LPV systems, to obtain LPV state-space models and robust gain-scheduled output feedback controllers. The methods to be developed in this project can effectively solve the challenges due to the strong nonlinear and multivariate nature of the PET dynamics. The developed control scheme can guarantee the accuracy and stability of the output parameters and spectra of these light sources, when subject to dramatic temperature fluctuations. Moreover, this project will also develop iterative learning control (ILC) approaches that do not rely on the surface spectral reflectance of the imaged objects under the illuminating light, so as to enhance the color contrast of the illuminated objects in a data-driven fashion. The application of the multi-spectral LED light source for imaging biological tissues will be investigated. The developed system identification and ILC techniques will respectively solve the difficulties in modeling the light source by first principles, and due to the unavailable surface reflectance parameters of the imaged object, and thereby pave the way for its successful applications in multi-spectral imaging, medical diagnosis and therapy.