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Research Papers

Parameter Estimation Using a Combined Variable Structure and Kalman Filtering Approach

[+] Author and Article Information
Saeid Habibi

Department of Mechanical Engineering, McMaster University, Hamilton, Ontario, Canada L8S 4L7saeiḏhabibi@engr.usask.ca

J. Dyn. Sys., Meas., Control 130(5), 051004 (Aug 01, 2008) (14 pages) doi:10.1115/1.2907393 History: Received August 25, 2005; Revised November 08, 2007; Published August 01, 2008

A method that is often used for parameter estimation is the extended Kalman filter (EKF). EKF is a model-based strategy that implicitly considers the effect of modeling uncertainties. This implicit consideration often leads to the tuning of the filter by trial and error. When formulated for parameter estimation, the “tuned” EKF becomes sensitive to uncertainties in its internal model. The EKF’s robustness can be improved by combining it with the recently proposed variable structure filter (VSF) concept. In a combined form, the modeling uncertainties no longer affect stability, but impact the performance and the quality of the estimation process. Furthermore, the VSF concept provides a secondary set of indicators of performance that is in addition to the estimation error and that pertains to the range of parametric uncertainties. As such, the robustness of the combined method and its multiple indicators of performance allow the use of intelligent adaptation for improving the performance of the estimation process. For real-time applications, online neural network adaptation may be used to improve the performance by progressively reducing specific modeling uncertainties in the system. In this paper, a new parameter estimation method that uses concepts associated with the EKF, the VSF, and neural network adaptation is introduced. The performance of this method is considered and discussed for applications that involve parameter estimation such as fault detection.

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Copyright © 2008 by American Society of Mechanical Engineers
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References

Figures

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Figure 1

Estimated and actual states

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Figure 2

Estimated and actual states with ω̂=30

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Figure 3

VSF state estimation concept

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Figure 4

Convergence of the estimated state trajectory due to VSF action

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Figure 5

Estimated and actual states with ω̂=30

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Figure 6

NARF flowchart augmented with comments specific to the example of Sec. 5

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Figure 7

Multilayered neural network

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Figure 8

Comparison of outer-loop estimation results for ω̂ using the range of neural network structures listed in Table 2

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Figure 9

Estimated and actual states using NARF

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Figure 10

Estimated and actual states after convergence of ω̂

Tables

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