TY - GEN
T1 - Interval Observers for Simultaneous State and Model Estimation of Partially Known Nonlinear Systems
AU - Khajenejad, Mohammad
AU - Jin, Zeyuan
AU - Yong, Sze Zheng
N1 - Funding Information:
This work is partially supported by NSF grant CNS-1943545 and an Early Career Faculty grant 80NSSC21K0071 from NASA’s Space Technology Research Grants Program.
Publisher Copyright:
© 2021 American Automatic Control Council.
PY - 2021/5/25
Y1 - 2021/5/25
N2 - We consider the problem of designing interval observers for partially unknown nonlinear systems with bounded noise signals that simultaneously estimate the system states and learn a model of the unknown dynamics. Leveraging affine abstraction methods and nonlinear decomposition functions, as well as a data-driven function over-approximation/abstraction approach to over-estimate the unknown dynamic model, our proposed observer recursively computes the maximal and minimal elements of the interval estimates that are proven to frame the true augmented states. Then, using observed output/measurement signals, the observer iteratively shrinks the intervals by eliminating estimates that are not compatible with the measurements. Moreover, given new interval estimates, the observer updates the over-approximation model of the unknown dynamics. Finally, we provide sufficient conditions for uniform boundedness of the sequence of interval estimate widths, i.e., for the stability of the designed observer.
AB - We consider the problem of designing interval observers for partially unknown nonlinear systems with bounded noise signals that simultaneously estimate the system states and learn a model of the unknown dynamics. Leveraging affine abstraction methods and nonlinear decomposition functions, as well as a data-driven function over-approximation/abstraction approach to over-estimate the unknown dynamic model, our proposed observer recursively computes the maximal and minimal elements of the interval estimates that are proven to frame the true augmented states. Then, using observed output/measurement signals, the observer iteratively shrinks the intervals by eliminating estimates that are not compatible with the measurements. Moreover, given new interval estimates, the observer updates the over-approximation model of the unknown dynamics. Finally, we provide sufficient conditions for uniform boundedness of the sequence of interval estimate widths, i.e., for the stability of the designed observer.
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U2 - 10.23919/ACC50511.2021.9483316
DO - 10.23919/ACC50511.2021.9483316
M3 - Conference contribution
AN - SCOPUS:85111931550
T3 - Proceedings of the American Control Conference
SP - 2848
EP - 2854
BT - 2021 American Control Conference, ACC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 American Control Conference, ACC 2021
Y2 - 25 May 2021 through 28 May 2021
ER -