Model Predictive Control for Linear Parameter Varying Systems Using a New Parameter Dependent Terminal Weighting Matrix
Summary :
In this paper, we propose a new robust model predictive control (MPC) technique for linear parameter varying (LPV) systems expressed as linear systems with feedback parameters. It is based on the minimization of the upper bound of finite horizon cost function using a new parameter dependent terminal weighting matrix. The proposed parameter dependent terminal weighting matrix for norm-bounded uncertain models provides a less conservative condition for terminal inequality. The optimization problem that satisfies the terminal inequality is solved by semi-definite programming involving linear matrix inequalities (LMIs). A numerical example is included to illustrate the effectiveness of the proposed method.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E89-A No.8 pp.2166-2172
- Publication Date
- 2006/08/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1093/ietfec/e89-a.8.2166
- Type of Manuscript
- PAPER
- Category
- Systems and Control
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Keyword
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Sangmoon LEE, Sangchul WON, "Model Predictive Control for Linear Parameter Varying Systems Using a New Parameter Dependent Terminal Weighting Matrix" in IEICE TRANSACTIONS on Fundamentals,
vol. E89-A, no. 8, pp. 2166-2172, August 2006, doi: 10.1093/ietfec/e89-a.8.2166.
Abstract: In this paper, we propose a new robust model predictive control (MPC) technique for linear parameter varying (LPV) systems expressed as linear systems with feedback parameters. It is based on the minimization of the upper bound of finite horizon cost function using a new parameter dependent terminal weighting matrix. The proposed parameter dependent terminal weighting matrix for norm-bounded uncertain models provides a less conservative condition for terminal inequality. The optimization problem that satisfies the terminal inequality is solved by semi-definite programming involving linear matrix inequalities (LMIs). A numerical example is included to illustrate the effectiveness of the proposed method.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e89-a.8.2166/_p
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@ARTICLE{e89-a_8_2166,
author={Sangmoon LEE, Sangchul WON, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Model Predictive Control for Linear Parameter Varying Systems Using a New Parameter Dependent Terminal Weighting Matrix},
year={2006},
volume={E89-A},
number={8},
pages={2166-2172},
abstract={In this paper, we propose a new robust model predictive control (MPC) technique for linear parameter varying (LPV) systems expressed as linear systems with feedback parameters. It is based on the minimization of the upper bound of finite horizon cost function using a new parameter dependent terminal weighting matrix. The proposed parameter dependent terminal weighting matrix for norm-bounded uncertain models provides a less conservative condition for terminal inequality. The optimization problem that satisfies the terminal inequality is solved by semi-definite programming involving linear matrix inequalities (LMIs). A numerical example is included to illustrate the effectiveness of the proposed method.},
keywords={},
doi={10.1093/ietfec/e89-a.8.2166},
ISSN={1745-1337},
month={August},}
Copy
TY - JOUR
TI - Model Predictive Control for Linear Parameter Varying Systems Using a New Parameter Dependent Terminal Weighting Matrix
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2166
EP - 2172
AU - Sangmoon LEE
AU - Sangchul WON
PY - 2006
DO - 10.1093/ietfec/e89-a.8.2166
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E89-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 2006
AB - In this paper, we propose a new robust model predictive control (MPC) technique for linear parameter varying (LPV) systems expressed as linear systems with feedback parameters. It is based on the minimization of the upper bound of finite horizon cost function using a new parameter dependent terminal weighting matrix. The proposed parameter dependent terminal weighting matrix for norm-bounded uncertain models provides a less conservative condition for terminal inequality. The optimization problem that satisfies the terminal inequality is solved by semi-definite programming involving linear matrix inequalities (LMIs). A numerical example is included to illustrate the effectiveness of the proposed method.
ER -
English
