Adaptive neural control for output feedback nonlinear systems using a barrier Lyapunov function

Beibei Ren, Shuzhi Sam Ge, Keng Peng Tee, Tong Heng Lee

Research output: Contribution to journalArticlepeer-review

545 Scopus citations

Abstract

In this brief, adaptive neural control is presented for a class of output feedback nonlinear systems in the presence of unknown functions. The unknown functions are handled via on-line neural network (NN) control using only output measurements. A barrier Lyapunov function (BLF) is introduced to address two open and challenging problems in the neuro-control area: 1) for any initial compact set, how to determine a priori the compact superset, on which NN approximation is valid; and 2) how to ensure that the arguments of the unknown functions remain within the specified compact superset. By ensuring boundedness of the BLF, we actively constrain the argument of the unknown functions to remain within a compact superset such that the NN approximation conditions hold. The semiglobal boundedness of all closed-loop signals is ensured, and the tracking error converges to a neighborhood of zero. Simulation results demonstrate the effectiveness of the proposed approach.

Original languageEnglish
Article number5499019
Pages (from-to)1339-1345
Number of pages7
JournalIEEE Transactions on Neural Networks
Volume21
Issue number8
DOIs
StatePublished - Aug 2010

Keywords

  • Barrier function
  • neural networks (NNs)
  • output feedback nonlinear systems
  • unknown functions

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