We have located links that may give you full text access.
Robust particle filter for state estimation using measurements with different types of gross errors.
ISA Transactions 2017 July
For industrial processes, the state estimation plays a key role in various applications, such as process monitoring and model based control. Although the particle filter (PF) is able to deal with nonlinear and non-Gaussian processes, it rarely considers the influence of measurements with gross errors, such as outliers, biases and drifts. Nevertheless, measurements of dynamical systems are often influenced by different types of gross errors. This paper proposes a robust PF approach, in which gross error identification is used to estimate magnitudes of gross error. The gross errors can be removed or compensated so that a feasible set of particle sampling can contain the true states of the system. The proposed robust PF approach is implemented on a complex nonlinear dynamic system, the free radical polymerization of styrene. The application results show that the proposed approach is an appealing alternative to solving PF estimation problems with measurements containing gross errors.
Full text links
Related Resources
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app
All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.
By using this service, you agree to our terms of use and privacy policy.
Your Privacy Choices
You can now claim free CME credits for this literature searchClaim now
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app