H∞Tracking Control of Descriptor Nonlinear System for Output PDFs of Stochastic Systems Based on B-Spline Neural Networks

Authors:
Haiqin Sun;Huiling Xu;Chenglin Wen
Affiliations:
Research Institute of Automation Southeast University, Nanjing, 210096, Email: l.guo@seu.edu.cn, P.R. China;Research Institute of Automation Southeast University, Nanjing, 210096, Email: l.guo@seu.edu.cn, P.R. China;School of Automation, Hangzhou Dianzi University, Hangzhou 310018, P.R. China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Year:
2007

Citing 4
Cited 1

Neurofuzzy adaptive modelling and control

Neurofuzzy adaptive modelling and control
H∞ control for descriptor systems: a matrix inequalities approach

Automatica (Journal of IFAC)
Bounded Dynamic Stochastic Systems: Modelling and Control

Bounded Dynamic Stochastic Systems: Modelling and Control
PID controller design for output PDFs of stochastic systems using linear matrix inequalities

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

EEG-based expert system using complexity measures and probability density function control in alpha sub-band

Integrated Computer-Aided Engineering

Quantified Score

Hi-index	0.00

Visualization

Abstract

For stochastic systems with non-Gaussian variables, a descriptor nonlinear system model based on linear B-spline approximation is first established. A new tracking strategy based on H∞state feedback control for the descriptor nonlinear system is proposed, with which the probability density functions (PDFs) tracking control problem of the non-Gaussian stochastic systems can be solved. Necessary and sufficient condition for the existence of H∞state feedback controller of the problem is presented by linear-matrix-inequality (LMI). Furthermore, simulations on particle distribution control problems are given to demonstrate the efficiency of the proposed approach and encouraging results have been obtained.