Robust PI Tracking Strategy for Output Probability Distributions Based on Uncertain B-Spline Neural Networks

Author

Wu, Linyao ; Zhang, Yumin ; Ma, Tao ; Guo, Lei

Author_Institution

Res. Inst. of Autom., Southeast Univ., Nanjing

Volume

3

fYear

2005

Firstpage

1831

Lastpage

1835

Abstract

This paper considers the robust tracking control problem for output stochastic distributions of dynamic non-Gaussian systems. By using the square root B-spline approximations with modelling errors, a robust constrained tracking control strategy with proportional-integral (PI) structure is investigated for a nonlinear weighting system in the presence of exogenous disturbances. The main objective is to make the output probability density functions (PDFs) to follow a target PDF. An LMI-based PI control algorithm is proposed to track the desired weight dynamics, where the robust peak-to-peak measure is applied to optimize the tracking performance and the state constraints system related to the B-spline expansion can be guaranteed. Rigorous stability and performance analysis is provided for the constrained weight tracking control problem

Keywords

PI control; approximation theory; linear matrix inequalities; neurocontrollers; robust control; splines (mathematics); stochastic systems; uncertain systems; LMI; dynamic nonGaussian systems; output probability distributions; probability density functions; proportional-integral structure; robust PI tracking strategy; square root B-spline approximations; uncertain B-spline neural networks; Control systems; Neural networks; Nonlinear dynamical systems; Pi control; Probability distribution; Robust control; Robustness; Spline; Stochastic processes; Target tracking;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks and Brain, 2005. ICNN&B '05. International Conference on

Conference_Location

Beijing

Print_ISBN

0-7803-9422-4

Type

conf

DOI

10.1109/ICNNB.2005.1614982

Filename

1614982