Vehicle Motion Estimation Using A Switched Gain Nonlinear Observer

R. Rajamani, W. Jeon, H. Movahedi, A. Zemouche

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Observer design for a nonlinear system in which the process dynamics equation are composed of nonlinear vector functions of scalar combinations of the states is considered. Assuming that the nonlinear functions have bounded derivatives, an observer design algorithm that requires solving just a single linear matrix inequality for exponentially convergent state estimation is developed. The developed algorithm works effectively when the involved nonlinear functions are monotonic. However, it fails when all or even some of the system functions are non-monotonic. Analytical results are presented to show that no solutions exist when all process dynamics functions are non-monotonic, no matter how small the Lipschitz constant or the Jacobian bounds of the nonlinearities. To overcome this limitation, a switched gain observer that switches between multiple constant observer gains is developed that can provide global exponentially stability for systems with non-monotonic nonlinear functions. The application of the developed hybrid observer is demonstrated to a motion estimation application involving vehicle position tracking on local roads and highways.

Original languageEnglish (US)
Title of host publication2020 American Control Conference, ACC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781538682661
StatePublished - Jul 2020
Event2020 American Control Conference, ACC 2020 - Denver, United States
Duration: Jul 1 2020Jul 3 2020

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Conference2020 American Control Conference, ACC 2020
Country/TerritoryUnited States

Bibliographical note

Funding Information:
This research was supported in part by funding from the National Science Foundation under Grant PFI 1631133.

Funding Information:
This research was funded in part by the US National Science Foundation, under grant PFI1631133.

Publisher Copyright:
© 2020 AACC.


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