A neurodynamic approach to model predictive control of piecewise linear systems

Zheng Yan; Jun Wang

doi:10.1109/ICICIP.2012.6391405

A neurodynamic approach to model predictive control of piecewise linear systems

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

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Abstract

This paper presents a neurodynamic approach to model predictive control (MPC) of constrained piecewise linear systems. A novel procedure for estimating uncertain system parameters of piecewise linear systems is proposed. The model predictive control problem is then formulated as a quadratic optimization problem. To realize the real-time optimization in MPC, a one-layer recurrent neural network is employed for solving the quadratic optimization problem during each sampling interval. The overall MPC approach is of low computational complexity. Simulation results are included to substantiate the effectiveness and usefulness of the proposed approach. © 2012 IEEE.

Original language	English
Title of host publication	ICICIP 2012 - 2012 3rd International Conference on Intelligent Control and Information Processing
Pages	463-468
DOIs	https://doi.org/10.1109/ICICIP.2012.6391405
Publication status	Published - 2012
Externally published	Yes
Event	2012 3rd International Conference on Intelligent Control and Information Processing, ICICIP 2012 - Dalian, China Duration: 15 Jul 2012 → 17 Jul 2012

Conference

Conference	2012 3rd International Conference on Intelligent Control and Information Processing, ICICIP 2012
Country/Territory	China
City	Dalian
Period	15/07/12 → 17/07/12

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@inproceedings{0422d9185ed94b828cf5a27dacb19526,

title = "A neurodynamic approach to model predictive control of piecewise linear systems",

abstract = "This paper presents a neurodynamic approach to model predictive control (MPC) of constrained piecewise linear systems. A novel procedure for estimating uncertain system parameters of piecewise linear systems is proposed. The model predictive control problem is then formulated as a quadratic optimization problem. To realize the real-time optimization in MPC, a one-layer recurrent neural network is employed for solving the quadratic optimization problem during each sampling interval. The overall MPC approach is of low computational complexity. Simulation results are included to substantiate the effectiveness and usefulness of the proposed approach. {\textcopyright} 2012 IEEE.",

author = "Zheng Yan and Jun Wang",

year = "2012",

doi = "10.1109/ICICIP.2012.6391405",

language = "English",

isbn = "9781457721427",

pages = "463--468",

booktitle = "ICICIP 2012 - 2012 3rd International Conference on Intelligent Control and Information Processing",

note = "2012 3rd International Conference on Intelligent Control and Information Processing, ICICIP 2012 ; Conference date: 15-07-2012 Through 17-07-2012",

}

Yan, Z & Wang, J 2012, A neurodynamic approach to model predictive control of piecewise linear systems. in ICICIP 2012 - 2012 3rd International Conference on Intelligent Control and Information Processing., 6391405, pp. 463-468, 2012 3rd International Conference on Intelligent Control and Information Processing, ICICIP 2012, Dalian, China, 15/07/12. https://doi.org/10.1109/ICICIP.2012.6391405

A neurodynamic approach to model predictive control of piecewise linear systems. / Yan, Zheng; Wang, Jun.
ICICIP 2012 - 2012 3rd International Conference on Intelligent Control and Information Processing. 2012. p. 463-468 6391405.

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

TY - GEN

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AU - Yan, Zheng

AU - Wang, Jun

PY - 2012

Y1 - 2012

N2 - This paper presents a neurodynamic approach to model predictive control (MPC) of constrained piecewise linear systems. A novel procedure for estimating uncertain system parameters of piecewise linear systems is proposed. The model predictive control problem is then formulated as a quadratic optimization problem. To realize the real-time optimization in MPC, a one-layer recurrent neural network is employed for solving the quadratic optimization problem during each sampling interval. The overall MPC approach is of low computational complexity. Simulation results are included to substantiate the effectiveness and usefulness of the proposed approach. © 2012 IEEE.

AB - This paper presents a neurodynamic approach to model predictive control (MPC) of constrained piecewise linear systems. A novel procedure for estimating uncertain system parameters of piecewise linear systems is proposed. The model predictive control problem is then formulated as a quadratic optimization problem. To realize the real-time optimization in MPC, a one-layer recurrent neural network is employed for solving the quadratic optimization problem during each sampling interval. The overall MPC approach is of low computational complexity. Simulation results are included to substantiate the effectiveness and usefulness of the proposed approach. © 2012 IEEE.

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DO - 10.1109/ICICIP.2012.6391405

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

SN - 9781457721427

SP - 463

EP - 468

BT - ICICIP 2012 - 2012 3rd International Conference on Intelligent Control and Information Processing

T2 - 2012 3rd International Conference on Intelligent Control and Information Processing, ICICIP 2012

Y2 - 15 July 2012 through 17 July 2012

ER -

A neurodynamic approach to model predictive control of piecewise linear systems

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