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确定采样型强跟踪滤波飞机舵面故障诊断与隔离
引用本文:马骏,倪世宏,解武杰,董文瀚.确定采样型强跟踪滤波飞机舵面故障诊断与隔离[J].控制理论与应用,2015,32(6):734-743.
作者姓名:马骏  倪世宏  解武杰  董文瀚
作者单位:空军工程大学航空航天工程学院,陕西西安,710038
基金项目:国家自然科学基金项目(60904038, 61273141), 航空基金项目(20141396012)资助.
摘    要:为了克服扩展多模型自适应估计中扩展卡尔曼滤波的理论局限性,多重渐消因子强跟踪改进引起的滤波发散现象以及多维高斯故障概率计算量大等问题,本文将一类基于确定解析采样近似方法的非线性次优高斯滤波与多模型自适应估计相结合,提出了改进的多重渐消因子强跟踪非线性滤波快速故障诊断方法.确定采样型滤波克服了扩展卡尔曼滤波的理论局限性;推导了等效多重渐消因子计算方法,避免了非线性系统雅克比矩阵的计算,提高了故障突变时的跟踪性能;提出了基于平方根分解的改进的一步预测协方差更新方程,保证了滤波稳定性;提出了基于欧几里得范数简化的故障概率计算方法,降低了计算量.通过对比仿真验证了3种不同非线性滤波算法及其强跟踪改进算法的有效性,故障诊断方法跟踪性强、速度快、精度高,具有较好的鲁棒性和稳定性.

关 键 词:故障诊断  多模型自适应估计  多重渐消因子  无迹卡尔曼滤波  中心差分卡尔曼滤波  容积卡尔曼滤波
收稿时间:1/9/2015 12:00:00 AM
修稿时间:2015/3/18 0:00:00

Deterministic sampling strong tracking filtering algorithms: fast detection and isolation for aircraft actuator fault
MA Jun,NI Shi-hong,XIE Wujie and DONG Wen-han.Deterministic sampling strong tracking filtering algorithms: fast detection and isolation for aircraft actuator fault[J].Control Theory & Applications,2015,32(6):734-743.
Authors:MA Jun  NI Shi-hong  XIE Wujie and DONG Wen-han
Affiliation:Aeronautics and Astronautics Engineering Institute, Air Force Engineering University,Aeronautics and Astronautics Engineering Institute, Air Force Engineering University,Aeronautics and Astronautics Engineering Institute, Air Force Engineering University,Aeronautics and Astronautics Engineering Institute, Air Force Engineering University
Abstract:The extended Kalman filter used in the extended multi-model adaptive estimation method has theoretical limitations; multiple fading factors may result in divergence of the strong tracking filter. Moreover, the fault probability calculation cost is large. In this paper, a class of deterministic sampling nonlinear suboptimal Gauss filters is combined with the multi-model adaptive estimation, and an improved nonlinear filter algorithm based on equivalent multiple fading factors is proposed for fast fault diagnosis. The theoretical limitation of the extended Kalman filter is eliminated by deterministic sampling filters. The equivalent multiple fading factor equations are derived, which avoid the calculation of the Jacobian matrix of the target nonlinear system. An improved update equation of the prediction covariance matrix based on the square-root decomposition is proposed, which ensures the stability of filter. Furthermore, based on the Euclidean norm, a fast fault isolation method that reduces the fault probability calculation cost is proposed. The simulation results show that the algorithm is more efficient and has better performance than the standard method.
Keywords:fault detection  multiple model adaptive estimation  multiple fading factors  unscented Kalman filter  central difference Kalman filter  cubature Kalman filter
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