基于多渐消因子强跟踪UKF 和约束AR模型的故障估计与预测

针对非线性系统中不可观测故障参数估计和预测问题, 提出一种基于多重渐消因子强跟踪无迹卡尔曼滤波(MSTUKF) 的状态和参数联合估计法, 通过引入多重渐消因子增强了对变化函数未知的故障参数的跟踪能力. 对于得到的故障参数估计值, 利用递推最小二乘法更新约束AR预测模型, 从而实现故障参数的在线估计与预测. 仿真结果表明, MSTUKF方法在故障参数估计精度上优于UKF 和单渐消因子强跟踪UKF, 约束AR模型的预测精度高于无约束条件下的预测精度.

Fault estimation and prediction based on multiple fading factors strong tracking UKF and constrained AR model

For the unmeasured fault parameters estimation and prediction problem of nonlinear systems, the state and parameter joint estimation algorithm based on multiple fading factors strong tracking unscented Kalman filter(MSTUKF) is presented. Multiple fading factors of MSTUKF are introduced to improve the tracking ability for fault parameters with the unknown changing function. With the fault parameters estimation of MSTUKF, the constrained AR prediction model is updated by recursive least squares. Online estimation and prediction of fault parameters are realized by using the proposed method. Simulation results show that the fault parameters estimation ability of MSTUKF is better than that of UKF and single fading factor strong tracking UKF, and the predicting accuracy of constrained AR model is superior to the unconstrained AR model.