17th International Symposium on
Mathematical Theory of Networks and Systems
Kyoto International Conference Hall, Kyoto, Japan, July 24-28, 2006

MTNS 2006 Paper Abstract


Paper TuP11.3

Hayakawa, Tomohisa (Tokyo Inst. of Tech.)

Adaptive Feedback Passification Control for Nonlinear Uncertain Systems Via Neural Networks

Scheduled for presentation during the Regular Session "Adaptive and Learning Control" (TuP11), Tuesday, July 25, 2006, 16:10−16:35, Room 104a

17th International Symposium on Mathematical Theory of Networks and Systems, July 24-28, 2006, Kyoto, Japan

This information is tentative and subject to change. Compiled on February 23, 2024

Keywords Neural networks, Stability analyisis, Robust adaptive control


A neural network adaptive control framework for nonlinear feedback passive systems is proposed. In light of a new recent approximation characterization, the neuro adaptive control framework guarantees, unlike standard neural network controllers guaranteeing ultimate boundedness, partial asymptotic stability of the closed-loop system, that is, asymptotic stability with respect to part of the closed-loop system states associated with the system plant states. The neuro adaptive controllers are constructed without requiring explicit knowledge of the system dynamics other than the assumption that the plant dynamics are strongly exponentially minimum phase and relative degree one. In this case, the approximation error of uncertain system nonlinearities lie in a small-gain type norm bounded conic sector. This allows us to show that the standard neural network output feedback controllers are in fact capable of achieving partial asymptotic stability with respect to the system equilibrium point.