Neural-network-based control design: An LMI approach

Suttipan Limanond, Jennie Si

Research output: Contribution to journalArticlepeer-review

128 Scopus citations


In this paper, we address a neural-network-based control design for a discrete-time nonlinear system. Our design approach is to approximate the nonlinear system with a multilayer perceptron of which the activation functions are of the sigmoid type symmetric to the origin. A linear difference inclusion representation is then established for this class of approximating neural networks and is used to design a state-feedback control law for the nonlinear system based on the certainty equivalence principle. The control design equations are shown to be a set of linear matrix inequalities where a convex optimization algorithm can be applied to determine the control signal. Further, the stability of the closed-loop is guaranteed in the sense that there exists a unique global attraction region in the neighborhood of the origin to which every trajectory of the closed-loop system converges. Finally, a simple example is presented so as to illustrate our control design procedure.

Original languageEnglish (US)
Pages (from-to)1422-1429
Number of pages8
JournalIEEE Transactions on Neural Networks
Issue number6
StatePublished - 1998


  • Linear matrix inequality
  • Multilayer perceptron
  • Nonlinear control
  • Stability analysis

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence


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