Model predictive control of nonlinear systems with unmodeled dynamics based on feedforward and recurrent neural networks

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Article number6222328
Pages (from-to)746-756
Journal / PublicationIEEE Transactions on Industrial Informatics
Issue number4
Publication statusPublished - 2012
Externally publishedYes


This paper presents new results on a neural network approach to nonlinear model predictive control. At first, a nonlinear system with unmodeled dynamics is decomposed by means of Jacobian linearization to an affine part and a higher-order unknown term. The unknown higher-order term resulted from the decomposition, together with the unmodeled dynamics of the original plant, are modeled by using a feedforward neural network via supervised learning. The optimization problem for nonlinear model predictive control is then formulated as a quadratic programming problem based on successive Jacobian linearization about varying operating points and iteratively solved by using a recurrent neural network called the simplified dual network. Simulation results are included to substantiate the effectiveness and illustrate the performance of the proposed approach. © 2012 IEEE.

Research Area(s)

  • Feedforward neural networks, model predictive control (MPC), real-time optimization, recurrent neural networks, supervised learning, unmodeled dynamics