Model predictive control for nonlinear affine systems based on the simplified dual neural network

Yunpeng Pan, Jun Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

24 Citations (Scopus)

Abstract

Model predictive control (MPC), also known as receding horizon control (RHC), is an advanced control strategy for optimizing the performance of control systems. For nonlinear systems, standard MPC schemes based on linearization would result in poor performance. In this paper, we propose an MPC scheme for nonlinear affine systems based on a recurrent neural network (RNN) called the simplified dual network. The proposed RNN-based approach is efficient and suitable for real-time MPC implementation in industrial applications. Simulation results are provided to demonstrate the effectiveness and efficiency of the proposed MPC scheme.

Original languageEnglish
Title of host publication2009 IEEE International Conference on Control Applications, CCA '09
Pages683-688
Number of pages6
DOIs
Publication statusPublished - 2009
Externally publishedYes
Event2009 IEEE International Conference on Control Applications, CCA '09 - Saint Petersburg, Russian Federation
Duration: 8 Jul 200910 Jul 2009

Publication series

NameProceedings of the IEEE International Conference on Control Applications

Conference

Conference2009 IEEE International Conference on Control Applications, CCA '09
Country/TerritoryRussian Federation
CitySaint Petersburg
Period8/07/0910/07/09

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