### Abstract

A novel neuro adaptive control framework for discrete-time multivariable nonlinear uncertain systems is developed. The proposed framework is Lyapunov-based and guarantees, instead of ultimate boundedness, partial asymptotic stability of the closed-loop system; that is, Lyapunov stability of the closed-loop system states and attraction with respect to the plant states. Unlike standard neural network approximation, we assume that the approximation error can be confined in a small gain-type norm-bounded conic sector over a compact set. This helps to couple tools from robust control with adaptive laws in discrete time to prove partial asymptotic stability of the closed-loop system. Finally, an illustrative numerical example is provided to demonstrate the efficacy of the proposed approach.

Original language | English (US) |
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Title of host publication | Proceedings of the 16th IFAC World Congress, IFAC 2005 |

Pages | 324-329 |

Number of pages | 6 |

State | Published - Dec 1 2005 |

Externally published | Yes |

Event | 16th Triennial World Congress of International Federation of Automatic Control, IFAC 2005 - Prague, Czech Republic Duration: Jul 3 2005 → Jul 8 2005 |

### Publication series

Name | IFAC Proceedings Volumes (IFAC-PapersOnline) |
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Volume | 16 |

ISSN (Print) | 1474-6670 |

### Other

Other | 16th Triennial World Congress of International Federation of Automatic Control, IFAC 2005 |
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Country | Czech Republic |

City | Prague |

Period | 7/3/05 → 7/8/05 |

### Keywords

- Adaptive control
- Asymptotic stability
- Discrete-time systems
- Lyapunov method
- Neural network
- Sector-bounded nonlinearities

### ASJC Scopus subject areas

- Control and Systems Engineering

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## Cite this

*Proceedings of the 16th IFAC World Congress, IFAC 2005*(pp. 324-329). (IFAC Proceedings Volumes (IFAC-PapersOnline); Vol. 16).