Loading [a11y]/accessibility-menu.js
Adaptive Neural Tracking for a Class of SISO Uncertain and Stochastic Nonlinear Systems | IEEE Conference Publication | IEEE Xplore

Adaptive Neural Tracking for a Class of SISO Uncertain and Stochastic Nonlinear Systems


Abstract:

Adaptive neural control schemes based on the backstepping technique are developed to solve the tracking control problem of a combined stochastic and uncertain nonlinear s...Show More

Abstract:

Adaptive neural control schemes based on the backstepping technique are developed to solve the tracking control problem of a combined stochastic and uncertain nonlinear system. As shown by an extensive stability analysis the proposed control scheme ensures that all the error variables are bounded in probability while the mean square tracking error becomes semiglobally uniformly ultimately bounded in an arbitrarily small area around the origin. The effectiveness of the design approach is illustrated by simulation results.
Date of Conference: 15-15 December 2005
Date Added to IEEE Xplore: 30 January 2006
Print ISBN:0-7803-9567-0
Print ISSN: 0191-2216
Conference Location: Seville, Spain

References

References is not available for this document.