Practical Approach for Kalman Filtering with Autocorrelated Noise Containing Uncertain Parameters

To improve the state estimation accuracy of nonlinear induction motor with uncertain parameters, a robust desensitized rank Kalman filtering (DRKF) is proposed to reduce state estimation error sensitivities to uncertain parameters. A new sensitivity function is defined, and a novel desensitized cost function for the deterministic sampling methods is designed to obtain an optimal gain matrix. The sensitivity propagation is summarized for deterministic sampling methods. Based on the rank sample rule, the sensitivity propagation method is given, and the DRKF algorithm is derived. Two dynamic behaviors of the induction motor with two uncertain stator and rotor resistances are simulated to demonstrate that the proposed DRKF has an excellent performance.

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Robust State Estimation of Induction Motor using Desensitized Rank Kalman Filter

10.36227/techrxiv.11966079 ◽

2020 ◽

Author(s):

Tai-shan Lou ◽

Dong-xuan Han ◽

Xiao-liang Yang ◽

Su-xia Jiang

Keyword(s):

State Estimation ◽

Induction Motor ◽

Kalman Filtering ◽

Sampling Methods ◽

Estimation Error ◽

Sensitivity Function ◽

Estimation Accuracy ◽

Uncertain Parameters ◽

Excellent Performance ◽

Deterministic Sampling

To improve the state estimation accuracy of nonlinear induction motor with uncertain parameters, a robust desensitized rank Kalman filtering (DRKF) is proposed to reduce state estimation error sensitivities to uncertain parameters. A new sensitivity function is defined, and a novel desensitized cost function for the deterministic sampling methods is designed to obtain an optimal gain matrix. The sensitivity propagation is summarized for deterministic sampling methods. Based on the rank sample rule, the sensitivity propagation method is given, and the DRKF algorithm is derived. Two dynamic behaviors of the induction motor with two uncertain stator and rotor resistances are simulated to demonstrate that the proposed DRKF has an excellent performance.

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Application of Interval Kalman Filtering Algorithm in Integrated Navigation Data Fusion

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.433-440.4059 ◽

2012 ◽

Vol 433-440 ◽

pp. 4059-4064

Author(s):

Yun Feng Ma

Keyword(s):

Kalman Filter ◽

Kalman Filtering ◽

Interval Matrix ◽

Uncertain Parameters ◽

Integrated Navigation ◽

System Matrix ◽

Inversion Algorithm ◽

Filtering Algorithm ◽

Kalman Filtering Algorithm ◽

Time Accuracy

The traditional Kalman filter cannot be used directly when some system parameters such as certain elements of the system matrix are not precisely known or gradually change with time. Some uncertain parameters can be described as an interval model. An interval Kalman filtering algorithm is studied in this paper, which can be used to process a system with uncertain parameters. A simple inversion algorithm of interval matrix has been applied and its statistic performances and iterative form are similar to those of traditional Kalman filter. Simulation results show that such filtering algorithm can provide the real time accuracy error estimation and can be applied to such kind of low-cost integrated navigation system.

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