Nonlinear optimal tracking with incomplete state information using finite-horizon State Dependent Riccati Equation (SDRE)

Ahmed Khamis; D. Subbaram Naidu

doi:10.1109/ACC.2014.6858589

Nonlinear optimal tracking with incomplete state information using finite-horizon State Dependent Riccati Equation (SDRE)

Ahmed Khamis
, D. Subbaram Naidu

Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

10 Scopus citations

Abstract

In this paper, an online technique for finite-horizon nonlinear stochastic tracking problems is presented. The idea of the proposed technique is to integrate the Kalman filter algorithm and the State Dependent Riccati Equation (SDRE) technique. Unlike the ordinary methods which deal with the linearized system, this technique will estimate the unmeasured states of the nonlinear system directly, and this will make the proposed technique effective for wide range of operating points. Numerical example is given to illustrate the effectiveness of the proposed technique.

Original language	English (US)
Title of host publication	2014 American Control Conference, ACC 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2420-2425
Number of pages	6
ISBN (Print)	9781479932726
DOIs	https://doi.org/10.1109/ACC.2014.6858589
State	Published - Jan 1 2014
Event	2014 American Control Conference, ACC 2014 - Portland, OR, United States Duration: Jun 4 2014 → Jun 6 2014

Other

Other	2014 American Control Conference, ACC 2014
Country/Territory	United States
City	Portland, OR
Period	6/4/14 → 6/6/14

Keywords

Kalman filtering
Nonlinear systems
Optimal control

Publisher link

10.1109/ACC.2014.6858589

Find It

Find It at U of M Libraries

Cite this

Nonlinear optimal tracking with incomplete state information using finite-horizon State Dependent Riccati Equation (SDRE). / Khamis, Ahmed; Naidu, D. Subbaram.
2014 American Control Conference, ACC 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 2420-2425 6858589.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Khamis, A & Naidu, DS 2014, Nonlinear optimal tracking with incomplete state information using finite-horizon State Dependent Riccati Equation (SDRE). in 2014 American Control Conference, ACC 2014., 6858589, Institute of Electrical and Electronics Engineers Inc., pp. 2420-2425, 2014 American Control Conference, ACC 2014, Portland, OR, United States, 6/4/14. https://doi.org/10.1109/ACC.2014.6858589

@inproceedings{035622bfe72d4234bd2c9cfc181f4ce0,

title = "Nonlinear optimal tracking with incomplete state information using finite-horizon State Dependent Riccati Equation (SDRE)",

abstract = "In this paper, an online technique for finite-horizon nonlinear stochastic tracking problems is presented. The idea of the proposed technique is to integrate the Kalman filter algorithm and the State Dependent Riccati Equation (SDRE) technique. Unlike the ordinary methods which deal with the linearized system, this technique will estimate the unmeasured states of the nonlinear system directly, and this will make the proposed technique effective for wide range of operating points. Numerical example is given to illustrate the effectiveness of the proposed technique.",

keywords = "Kalman filtering, Nonlinear systems, Optimal control",

author = "Ahmed Khamis and Naidu, \{D. Subbaram\}",

year = "2014",

month = jan,

day = "1",

doi = "10.1109/ACC.2014.6858589",

language = "English (US)",

isbn = "9781479932726",

pages = "2420--2425",

booktitle = "2014 American Control Conference, ACC 2014",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "2014 American Control Conference, ACC 2014 ; Conference date: 04-06-2014 Through 06-06-2014",

}

TY - GEN

T1 - Nonlinear optimal tracking with incomplete state information using finite-horizon State Dependent Riccati Equation (SDRE)

AU - Khamis, Ahmed

AU - Naidu, D. Subbaram

PY - 2014/1/1

Y1 - 2014/1/1

N2 - In this paper, an online technique for finite-horizon nonlinear stochastic tracking problems is presented. The idea of the proposed technique is to integrate the Kalman filter algorithm and the State Dependent Riccati Equation (SDRE) technique. Unlike the ordinary methods which deal with the linearized system, this technique will estimate the unmeasured states of the nonlinear system directly, and this will make the proposed technique effective for wide range of operating points. Numerical example is given to illustrate the effectiveness of the proposed technique.

AB - In this paper, an online technique for finite-horizon nonlinear stochastic tracking problems is presented. The idea of the proposed technique is to integrate the Kalman filter algorithm and the State Dependent Riccati Equation (SDRE) technique. Unlike the ordinary methods which deal with the linearized system, this technique will estimate the unmeasured states of the nonlinear system directly, and this will make the proposed technique effective for wide range of operating points. Numerical example is given to illustrate the effectiveness of the proposed technique.

KW - Kalman filtering

KW - Nonlinear systems

KW - Optimal control

UR - https://www.scopus.com/pages/publications/84905674494

UR - https://www.scopus.com/pages/publications/84905674494#tab=citedBy

U2 - 10.1109/ACC.2014.6858589

DO - 10.1109/ACC.2014.6858589

M3 - Conference contribution

AN - SCOPUS:84905674494

SN - 9781479932726

SP - 2420

EP - 2425

BT - 2014 American Control Conference, ACC 2014

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2014 American Control Conference, ACC 2014

Y2 - 4 June 2014 through 6 June 2014

ER -

Nonlinear optimal tracking with incomplete state information using finite-horizon State Dependent Riccati Equation (SDRE)

Abstract

Other

Keywords

Publisher link

Find It

Other files and links

Fingerprint

Cite this