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

Zheng Yan, Jun Wang

https://doi.org/10.1109/iecon.2011.6119669

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Publication Date: Nov 1, 2011

Citations: 8

Affiliation: Chinese University of Hong Kong

#Nonlinear Model Predictive Control #Nonlinear Model Predictive Control Problem + Show 8 more

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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.

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