A neural network approach to nonlinear model predictive control

Zheng Yan; Jun Wang

doi:10.1109/IECON.2011.6119669

A neural network approach to nonlinear model predictive control

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

9 Citations (Scopus)

Abstract

This paper proposes a neural network approach to nonlinear model predictive control (NMPC). The NMPC problem is formulated as a convex programming problem via Jacobain linearization. The unknown high-order term associated with the linearization is estimated by using a feedforward neural network via supervised learning. The convex optimization problem involved in MPC is solved by using a recurrent neural network. Simulation results are provided to demonstrate the performance of the approach. © 2011 IEEE.

Original language	English
Title of host publication	IECON Proceedings (Industrial Electronics Conference)
Pages	2305-2310
DOIs	https://doi.org/10.1109/IECON.2011.6119669
Publication status	Published - 2011
Externally published	Yes
Event	37th Annual Conference of the IEEE Industrial Electronics Society, IECON 2011 - Melbourne, VIC, Australia Duration: 7 Nov 2011 → 10 Nov 2011

Conference

Conference	37th Annual Conference of the IEEE Industrial Electronics Society, IECON 2011
Place	Australia
City	Melbourne, VIC
Period	7/11/11 → 10/11/11

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title = "A neural network approach to nonlinear model predictive control",

abstract = "This paper proposes a neural network approach to nonlinear model predictive control (NMPC). The NMPC problem is formulated as a convex programming problem via Jacobain linearization. The unknown high-order term associated with the linearization is estimated by using a feedforward neural network via supervised learning. The convex optimization problem involved in MPC is solved by using a recurrent neural network. Simulation results are provided to demonstrate the performance of the approach. {\textcopyright} 2011 IEEE.",

author = "Zheng Yan and Jun Wang",

year = "2011",

doi = "10.1109/IECON.2011.6119669",

language = "English",

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AB - This paper proposes a neural network approach to nonlinear model predictive control (NMPC). The NMPC problem is formulated as a convex programming problem via Jacobain linearization. The unknown high-order term associated with the linearization is estimated by using a feedforward neural network via supervised learning. The convex optimization problem involved in MPC is solved by using a recurrent neural network. Simulation results are provided to demonstrate the performance of the approach. © 2011 IEEE.

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DO - 10.1109/IECON.2011.6119669

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

SN - 9781612849720

SP - 2305

EP - 2310

BT - IECON Proceedings (Industrial Electronics Conference)

T2 - 37th Annual Conference of the IEEE Industrial Electronics Society, IECON 2011

Y2 - 7 November 2011 through 10 November 2011

ER -

A neural network approach to nonlinear model predictive control

Abstract

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