Robust model predictive control of nonlinear affine systems based on a two-layer recurrent neural network

Zheng Yan; Jun Wang

doi:10.1109/IJCNN.2011.6033195

Robust model predictive control of nonlinear affine systems based on a two-layer recurrent neural network

Zheng Yan
, Jun Wang

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

14 Citations (Scopus)

Abstract

A robust model predictive control (MPC) method is proposed for nonlinear affine systems with bounded disturbances. The robust MPC technique requires on-line solution of a minimax optimal control problem. The minimax strategy means that worst-case performance with respect to uncertainties is optimized. The minimax optimization problem involved in robust MPC is reformulated to a minimization problem and then is solved by using a two-layer recurrent neural network. Simulation examples are included to illustrate the effectiveness of the proposed method. © 2011 IEEE.

Original language	English
Title of host publication	Proceedings of the International Joint Conference on Neural Networks
Pages	24-29
DOIs	https://doi.org/10.1109/IJCNN.2011.6033195
Publication status	Published - 2011
Externally published	Yes
Event	2011 International Joint Conference on Neural Network, IJCNN 2011 - San Jose, CA, United States Duration: 31 Jul 2011 → 5 Aug 2011

Conference

Conference	2011 International Joint Conference on Neural Network, IJCNN 2011
Place	United States
City	San Jose, CA
Period	31/07/11 → 5/08/11

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10.1109/IJCNN.2011.6033195

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title = "Robust model predictive control of nonlinear affine systems based on a two-layer recurrent neural network",

abstract = "A robust model predictive control (MPC) method is proposed for nonlinear affine systems with bounded disturbances. The robust MPC technique requires on-line solution of a minimax optimal control problem. The minimax strategy means that worst-case performance with respect to uncertainties is optimized. The minimax optimization problem involved in robust MPC is reformulated to a minimization problem and then is solved by using a two-layer recurrent neural network. Simulation examples are included to illustrate the effectiveness of the proposed method. {\textcopyright} 2011 IEEE.",

author = "Zheng Yan and Jun Wang",

year = "2011",

doi = "10.1109/IJCNN.2011.6033195",

language = "English",

isbn = "9781457710865",

pages = "24--29",

booktitle = "Proceedings of the International Joint Conference on Neural Networks",

note = "2011 International Joint Conference on Neural Network, IJCNN 2011 ; Conference date: 31-07-2011 Through 05-08-2011",

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Yan, Z & Wang, J 2011, Robust model predictive control of nonlinear affine systems based on a two-layer recurrent neural network. in Proceedings of the International Joint Conference on Neural Networks., 6033195, pp. 24-29, 2011 International Joint Conference on Neural Network, IJCNN 2011, San Jose, CA, United States, 31/07/11. https://doi.org/10.1109/IJCNN.2011.6033195

Robust model predictive control of nonlinear affine systems based on a two-layer recurrent neural network. / Yan, Zheng; Wang, Jun.
Proceedings of the International Joint Conference on Neural Networks. 2011. p. 24-29 6033195.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

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AU - Wang, Jun

PY - 2011

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N2 - A robust model predictive control (MPC) method is proposed for nonlinear affine systems with bounded disturbances. The robust MPC technique requires on-line solution of a minimax optimal control problem. The minimax strategy means that worst-case performance with respect to uncertainties is optimized. The minimax optimization problem involved in robust MPC is reformulated to a minimization problem and then is solved by using a two-layer recurrent neural network. Simulation examples are included to illustrate the effectiveness of the proposed method. © 2011 IEEE.

AB - A robust model predictive control (MPC) method is proposed for nonlinear affine systems with bounded disturbances. The robust MPC technique requires on-line solution of a minimax optimal control problem. The minimax strategy means that worst-case performance with respect to uncertainties is optimized. The minimax optimization problem involved in robust MPC is reformulated to a minimization problem and then is solved by using a two-layer recurrent neural network. Simulation examples are included to illustrate the effectiveness of the proposed method. © 2011 IEEE.

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DO - 10.1109/IJCNN.2011.6033195

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781457710865

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EP - 29

BT - Proceedings of the International Joint Conference on Neural Networks

T2 - 2011 International Joint Conference on Neural Network, IJCNN 2011

Y2 - 31 July 2011 through 5 August 2011

ER -

Robust model predictive control of nonlinear affine systems based on a two-layer recurrent neural network

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