Optimizing semilinear representations for State-dependent Riccati Equation-based feedback control

S. Dolgov; D. Kalise; L. Saluzzi

doi:10.1016/j.ifacol.2022.11.104

Optimizing semilinear representations for State-dependent Riccati Equation-based feedback control

S. Dolgov, D. Kalise, L. Saluzzi

Research output: Contribution to journal › Conference article › peer-review

Abstract

An optimized variant of the State Dependent Riccati Equations (SDREs) approach for nonlinear optimal feedback stabilization is presented. The proposed method is based on the construction of equivalent semilinear representations associated to the dynamics and their affine combination. The optimal combination is chosen to minimize the discrepancy between the SDRE control and the optimal feedback law stemming from the solution of the corresponding Hamilton Jacobi Bellman (HJB) equation. Numerical experiments assess effectiveness of the method in terms of stability of the closed-loop with near-to-optimal performance.

Original language	English
Pages (from-to)	510-515
Number of pages	6
Journal	IFAC-PapersOnLine
Volume	55
Issue number	30
Early online date	23 Nov 2022
DOIs	https://doi.org/10.1016/j.ifacol.2022.11.104
Publication status	Published - 31 Dec 2022
Event	25th IFAC Symposium on Mathematical Theory of Networks and Systems, MTNS 2022 - Bayreuthl, Germany Duration: 12 Sept 2022 → 16 Sept 2022

Keywords

feedback control
Hamilton Jacobi Bellman equation
nonlinear optimal control
State Dependent Riccati equation

ASJC Scopus subject areas

Control and Systems Engineering

Access to Document

10.1016/j.ifacol.2022.11.104Licence: CC BY-NC-ND

Cite this

@article{4a89d713c5a347d2ba82d1976545937b,

title = "Optimizing semilinear representations for State-dependent Riccati Equation-based feedback control",

abstract = "An optimized variant of the State Dependent Riccati Equations (SDREs) approach for nonlinear optimal feedback stabilization is presented. The proposed method is based on the construction of equivalent semilinear representations associated to the dynamics and their affine combination. The optimal combination is chosen to minimize the discrepancy between the SDRE control and the optimal feedback law stemming from the solution of the corresponding Hamilton Jacobi Bellman (HJB) equation. Numerical experiments assess effectiveness of the method in terms of stability of the closed-loop with near-to-optimal performance.",

keywords = "feedback control, Hamilton Jacobi Bellman equation, nonlinear optimal control, State Dependent Riccati equation",

author = "S. Dolgov and D. Kalise and L. Saluzzi",

note = "This work was supported by the UK Engineering and Physical Sciences Research Council (EPRSC) grant EP/V04771X/1.DK was additionally supported by EPSRC grants EP/T024429/1, and EP/V025899/1; 25th IFAC Symposium on Mathematical Theory of Networks and Systems, MTNS 2022 ; Conference date: 12-09-2022 Through 16-09-2022",

year = "2022",

month = dec,

day = "31",

doi = "10.1016/j.ifacol.2022.11.104",

language = "English",

volume = "55",

pages = "510--515",

journal = "IFAC-PapersOnLine",

issn = "1474-6670",

publisher = "Elsevier",

number = "30",

}

TY - JOUR

T1 - Optimizing semilinear representations for State-dependent Riccati Equation-based feedback control

AU - Dolgov, S.

AU - Kalise, D.

AU - Saluzzi, L.

N1 - This work was supported by the UK Engineering and Physical Sciences Research Council (EPRSC) grant EP/V04771X/1.DK was additionally supported by EPSRC grants EP/T024429/1, and EP/V025899/1

PY - 2022/12/31

Y1 - 2022/12/31

N2 - An optimized variant of the State Dependent Riccati Equations (SDREs) approach for nonlinear optimal feedback stabilization is presented. The proposed method is based on the construction of equivalent semilinear representations associated to the dynamics and their affine combination. The optimal combination is chosen to minimize the discrepancy between the SDRE control and the optimal feedback law stemming from the solution of the corresponding Hamilton Jacobi Bellman (HJB) equation. Numerical experiments assess effectiveness of the method in terms of stability of the closed-loop with near-to-optimal performance.

AB - An optimized variant of the State Dependent Riccati Equations (SDREs) approach for nonlinear optimal feedback stabilization is presented. The proposed method is based on the construction of equivalent semilinear representations associated to the dynamics and their affine combination. The optimal combination is chosen to minimize the discrepancy between the SDRE control and the optimal feedback law stemming from the solution of the corresponding Hamilton Jacobi Bellman (HJB) equation. Numerical experiments assess effectiveness of the method in terms of stability of the closed-loop with near-to-optimal performance.

KW - feedback control

KW - Hamilton Jacobi Bellman equation

KW - nonlinear optimal control

KW - State Dependent Riccati equation

UR - http://www.scopus.com/inward/record.url?scp=85144828239&partnerID=8YFLogxK

U2 - 10.1016/j.ifacol.2022.11.104

DO - 10.1016/j.ifacol.2022.11.104

M3 - Conference article

AN - SCOPUS:85144828239

SN - 1474-6670

VL - 55

SP - 510

EP - 515

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

IS - 30

T2 - 25th IFAC Symposium on Mathematical Theory of Networks and Systems, MTNS 2022

Y2 - 12 September 2022 through 16 September 2022

ER -

Optimizing semilinear representations for State-dependent Riccati Equation-based feedback control

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Overcoming the curse of dimensionality in dynamic programming by tensor decompositions

Cite this

Optimizing semilinear representations for State-dependent Riccati Equation-based feedback control

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Projects

Overcoming the curse of dimensionality in dynamic programming by tensor decompositions

Cite this