IEICE TRANSACTIONS on Fundamentals
Discrete-Time Nonlinear Optimal Control via Generating Functions
Summary :
This paper develops the generating function method for the discrete-time nonlinear optimal control problem. This method can analytically give the optimal input as state feedforward control in terms of the generating functions. Since the generating functions are nonlinear, we also develop numerical implementations to find their Taylor series expressions. This finally gives optimal solutions expressed only in terms of the pre-computed generating function coefficients and state boundary conditions, such that it is useful for the on-demand optimal solution generation for different boundary conditions. Examples demonstrate the effectiveness of the developed method.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E99-A No.11 pp.2037-2048
- Publication Date
- 2016/11/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E99.A.2037
- Type of Manuscript
- PAPER
- Category
- Systems and Control
Authors
Dijian CHEN
Nagoya University
Kenji FUJIMOTO
Kyoto University
Tatsuya SUZUKI
Nagoya University
Keyword
Latest Issue
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Dijian CHEN, Kenji FUJIMOTO, Tatsuya SUZUKI, "Discrete-Time Nonlinear Optimal Control via Generating Functions" in IEICE TRANSACTIONS on Fundamentals,
vol. E99-A, no. 11, pp. 2037-2048, November 2016, doi: 10.1587/transfun.E99.A.2037.
Abstract: This paper develops the generating function method for the discrete-time nonlinear optimal control problem. This method can analytically give the optimal input as state feedforward control in terms of the generating functions. Since the generating functions are nonlinear, we also develop numerical implementations to find their Taylor series expressions. This finally gives optimal solutions expressed only in terms of the pre-computed generating function coefficients and state boundary conditions, such that it is useful for the on-demand optimal solution generation for different boundary conditions. Examples demonstrate the effectiveness of the developed method.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E99.A.2037/_p
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@ARTICLE{e99-a_11_2037,
author={Dijian CHEN, Kenji FUJIMOTO, Tatsuya SUZUKI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Discrete-Time Nonlinear Optimal Control via Generating Functions},
year={2016},
volume={E99-A},
number={11},
pages={2037-2048},
abstract={This paper develops the generating function method for the discrete-time nonlinear optimal control problem. This method can analytically give the optimal input as state feedforward control in terms of the generating functions. Since the generating functions are nonlinear, we also develop numerical implementations to find their Taylor series expressions. This finally gives optimal solutions expressed only in terms of the pre-computed generating function coefficients and state boundary conditions, such that it is useful for the on-demand optimal solution generation for different boundary conditions. Examples demonstrate the effectiveness of the developed method.},
keywords={},
doi={10.1587/transfun.E99.A.2037},
ISSN={1745-1337},
month={November},}
Copy
TY - JOUR
TI - Discrete-Time Nonlinear Optimal Control via Generating Functions
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2037
EP - 2048
AU - Dijian CHEN
AU - Kenji FUJIMOTO
AU - Tatsuya SUZUKI
PY - 2016
DO - 10.1587/transfun.E99.A.2037
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E99-A
IS - 11
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - November 2016
AB - This paper develops the generating function method for the discrete-time nonlinear optimal control problem. This method can analytically give the optimal input as state feedforward control in terms of the generating functions. Since the generating functions are nonlinear, we also develop numerical implementations to find their Taylor series expressions. This finally gives optimal solutions expressed only in terms of the pre-computed generating function coefficients and state boundary conditions, such that it is useful for the on-demand optimal solution generation for different boundary conditions. Examples demonstrate the effectiveness of the developed method.
ER -
English
