Skip to Main Content
Nonlinear Filters
book

Nonlinear Filters

by Peyman Setoodeh, Saeid Habibi, Simon Haykin
April 2022
Intermediate to advanced content levelIntermediate to advanced
304 pages
8h 12m
English
Wiley
Content preview from Nonlinear Filters

7Smooth Variable‐Structure Filter

7.1 Introduction

The smooth variable‐structure filter (SVSF) algorithm has been derived based on a stability theorem [97]. Inspired by the variable‐structure control (VSC) theory, the SVSF uses an inherent switching action to guarantee convergence of the estimated states to within a neighborhood of their true values. Similar to the mentioned Bayesian filters in the previous chapters, the SVSF has been formulated in a predictor–corrector form [56, 58, 62, 98]. Robustness against bounded uncertainties is an inherent characteristic of the VSC, which has been inherited by the SVSF [99]. The distinguishing features of the SVSF from other filters can be summarized as follows [97]:

  • The SVSF takes advantage of the inherent robustness of the VSC against bounded uncertainties. Hence, its convergence can be guaranteed for bounded uncertainty and noise. Moreover, a fairly precise estimate of the upper bound on the uncertainties will enhance the performance of the SVSF.
  • Unlike other filtering strategies that implicitly consider uncertainty and rely on trial and error for tuning, the SVSF formulation allows for explicit identification of the source of uncertainty and assigning a bound to it. Taking account of this information in the design, alleviates tuning by trial and error to a large extent.
  • In order to quantify the degree of uncertainty and modeling mismatch associated with each estimated state or parameter, the SVSF uses a secondary set of performance ...
Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Start your free trial

You might also like

Linear Synchronous Motors, 2nd Edition

Linear Synchronous Motors, 2nd Edition

Jacek F. Gieras, Zbigniew J. Piech, Bronislaw Tomczuk
Power Converters and AC Electrical Drives with Linear Neural Networks

Power Converters and AC Electrical Drives with Linear Neural Networks

Maurizio Cirrincione, Marcello Pucci, Gianpaolo Vitale
Multilevel Converters for Industrial Applications

Multilevel Converters for Industrial Applications

Sergio Alberto Gonzalez, Santiago Andres Verne, Maria Ines Valla

Publisher Resources

ISBN: 9781118835814Purchase Link