A robust fault diagnosis approach is developed by incorporating a set-membership identification (SMI) method. A class of systems with linear models in the form of fault related parameters is investigated, with model u...A robust fault diagnosis approach is developed by incorporating a set-membership identification (SMI) method. A class of systems with linear models in the form of fault related parameters is investigated, with model uncertainties and parameter variations taken into account explicitly and treated as bounded errors. An ellipsoid bounding set-membership identification algorithm is proposed to propagate bounded uncertainties rigorously and the guaranteed feasible set of faults parameters enveloping true parameter values is given. Faults arised from abrupt parameter variations can be detected and isolated on-line by consistency check between predicted and observed parameter sets obtained in the identification procedure. The proposed approach provides the improved robustness with its ability to distinguish real faults from model uncertainties, which comes with the inherent guaranteed robustness of the set-membership framework. Efforts are also made in this work to balance between conservativeness and computation complexity of the overall algorithm. Simulation results for the mobile robot with several slipping faults scenarios demonstrate the correctness of the proposed approach for faults detection and isolation (FDI).展开更多
从退化图像中恢复原始干净图像是一个经典的病态反问题,正则化技术是解决此问题的主流方法之一。它将解图像限定在一个正则空间中,复原图像即是退化图像在正则空间中的投影,但是针对不同图像选择合适的正则空间是一个难点。为提高正则...从退化图像中恢复原始干净图像是一个经典的病态反问题,正则化技术是解决此问题的主流方法之一。它将解图像限定在一个正则空间中,复原图像即是退化图像在正则空间中的投影,但是针对不同图像选择合适的正则空间是一个难点。为提高正则化模型的适应性,更为精细地建模不同特征图像,基于模糊集理论,提出了一个自适应复合正则化模型。首先采用学习算法计算图像对于不同正则空间的隶属度,然后选择隶属度最大的前s个空间,以隶属度为权重建立自适应复合正则化模型,最后采用ADMM(Alternating Direction Method of Multipliers)算法对模型进行求解。实验结果表明,对于不同的图像,模型可以很好地选择合适的正则空间,得到满意的复原效果。展开更多
基于未知但有界噪声假设的集员滤波器为传统的概率化滤波方法提供了一种可行的替代选择,然而其潜在的计算负担和保守性考虑制约了该方法的实际应用.本文提出一种新的基于保证定界椭球近似的改进集员滤波方法,用于解决针对非线性系统的...基于未知但有界噪声假设的集员滤波器为传统的概率化滤波方法提供了一种可行的替代选择,然而其潜在的计算负担和保守性考虑制约了该方法的实际应用.本文提出一种新的基于保证定界椭球近似的改进集员滤波方法,用于解决针对非线性系统的状态估计问题,在保证实时性的前提下降低了算法的保守性.首先,对非线性模型进行线性化处理,采用DC(Difference of convex)规划方法对线性化误差进行外包定界,并通过椭球近似将其融合到系统噪声中;在此基础上提出了一种结合了椭球直和计算和基于迭代外定界椭球算法的椭球-带交集计算所构成的经典预测-更新步骤来估计得到状态的可行椭球集.与常规的非线性扩展集员滤波方法的仿真比较表明了本文所提出算法的有效性和改进性能.展开更多
基金supported by the National Natural Science Foundation of China(616732546157310061573101)
文摘A robust fault diagnosis approach is developed by incorporating a set-membership identification (SMI) method. A class of systems with linear models in the form of fault related parameters is investigated, with model uncertainties and parameter variations taken into account explicitly and treated as bounded errors. An ellipsoid bounding set-membership identification algorithm is proposed to propagate bounded uncertainties rigorously and the guaranteed feasible set of faults parameters enveloping true parameter values is given. Faults arised from abrupt parameter variations can be detected and isolated on-line by consistency check between predicted and observed parameter sets obtained in the identification procedure. The proposed approach provides the improved robustness with its ability to distinguish real faults from model uncertainties, which comes with the inherent guaranteed robustness of the set-membership framework. Efforts are also made in this work to balance between conservativeness and computation complexity of the overall algorithm. Simulation results for the mobile robot with several slipping faults scenarios demonstrate the correctness of the proposed approach for faults detection and isolation (FDI).
文摘从退化图像中恢复原始干净图像是一个经典的病态反问题,正则化技术是解决此问题的主流方法之一。它将解图像限定在一个正则空间中,复原图像即是退化图像在正则空间中的投影,但是针对不同图像选择合适的正则空间是一个难点。为提高正则化模型的适应性,更为精细地建模不同特征图像,基于模糊集理论,提出了一个自适应复合正则化模型。首先采用学习算法计算图像对于不同正则空间的隶属度,然后选择隶属度最大的前s个空间,以隶属度为权重建立自适应复合正则化模型,最后采用ADMM(Alternating Direction Method of Multipliers)算法对模型进行求解。实验结果表明,对于不同的图像,模型可以很好地选择合适的正则空间,得到满意的复原效果。
文摘基于未知但有界噪声假设的集员滤波器为传统的概率化滤波方法提供了一种可行的替代选择,然而其潜在的计算负担和保守性考虑制约了该方法的实际应用.本文提出一种新的基于保证定界椭球近似的改进集员滤波方法,用于解决针对非线性系统的状态估计问题,在保证实时性的前提下降低了算法的保守性.首先,对非线性模型进行线性化处理,采用DC(Difference of convex)规划方法对线性化误差进行外包定界,并通过椭球近似将其融合到系统噪声中;在此基础上提出了一种结合了椭球直和计算和基于迭代外定界椭球算法的椭球-带交集计算所构成的经典预测-更新步骤来估计得到状态的可行椭球集.与常规的非线性扩展集员滤波方法的仿真比较表明了本文所提出算法的有效性和改进性能.
基金国家自然科学基金(the National Natural Science Foundation of China under Grant No.60564001)教育部留学回国人员科研启动基金(The Project-sponsoredby SRF for ROCS+1 种基金SEMChina under Grant No.教育司留[2004]527号)。
