Kernel-based simultaneous parameter-state estimation for continuous-time systems
File(s)
Accepted version
Author(s)
Li, Peng
Boem, Francesca
Pin, Gilberto
Parisini, Thomas
Type
Journal Article
Abstract
In this note, the problem of jointly estimating thestate and the parameters of continuous-time systems is addressed.Making use of suitably designed Volterra integral operators,the proposed estimator does not need the availability of time-derivatives of the measurable signals and the dependence ontheunknown initial conditions is removed. As a result, the estimatesconverge to the true values in arbitrarily short time in noise-freescenario. In the presence of bounded measurement and processdisturbances, the estimation error is shown to be bounded. Thenumerical implementation aspects are dealt with and extensivesimulation results are provides showing the effectivenessof theestimator.
Date Issued
2020-07-01
Date Acceptance
2019-10-14
Citation
IEEE Transactions on Automatic Control, 2020, 65 (7), pp.3053-3059
ISSN
0018-9286
Publisher
Institute of Electrical and Electronics Engineers
Start Page
3053
End Page
3059
Journal / Book Title
IEEE Transactions on Automatic Control
Volume
65
Issue
7
Copyright Statement
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Subjects
Science & Technology
Technology
Automation & Control Systems
Engineering, Electrical & Electronic
Engineering
Kernel
Convergence
Estimation
Noise measurement
Pins
Simulation
Power systems
Fixed-time convergence
parameter-state joint estimation
volterra integral operator
IDENTIFICATION
PERSISTENCY
EXCITATION
OBSERVER
0102 Applied Mathematics
0906 Electrical and Electronic Engineering
0913 Mechanical Engineering
Industrial Engineering & Automation
Publication Status
Published
Date Publish Online
2019-11-13