[2309.16159] Adaptive Real-Time Numerical Differentiation with Variable-Rate Forgetting and Exponential Resetting
Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Electrical Engineering and Systems Science > Systems and Control

arXiv:2309.16159 (eess)
[Submitted on 28 Sep 2023]

Title:Adaptive Real-Time Numerical Differentiation with Variable-Rate Forgetting and Exponential Resetting

Authors:Shashank Verma, Brian Lai, Dennis S. Bernstein
View PDF
Abstract:Digital PID control requires a differencing operation to implement the D gain. In order to suppress the effects of noisy data, the traditional approach is to filter the data, where the frequency response of the filter is adjusted manually based on the characteristics of the sensor noise. The present paper considers the case where the characteristics of the sensor noise change over time in an unknown way. This problem is addressed by applying adaptive real-time numerical differentiation based on adaptive input and state estimation (AISE). The contribution of this paper is to extend AISE to include variable-rate forgetting with exponential resetting, which allows AISE to more rapidly respond to changing noise characteristics while enforcing the boundedness of the covariance matrix used in recursive least squares.
Subjects: Systems and Control (eess.SY); Signal Processing (eess.SP)
Cite as: arXiv:2309.16159 [eess.SY]
  (or arXiv:2309.16159v1 [eess.SY] for this version)
  https://doi.org/10.48550/arXiv.2309.16159
Journal reference: 2024 American Control Conference (ACC), 3103-3108

Submission history

From: Shashank Verma [view email]
[v1] Thu, 28 Sep 2023 04:19:47 UTC (3,871 KB)
Full-text links:

Access Paper:

  • View PDF
  • TeX Source
  • Other Formats
view license
Current browse context:
eess.SY
< prev   |   next >
new | recent | 2023-09
Change to browse by:
cs
cs.SY
eess
eess.SP

References & Citations

export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)