Abstract
This paper presents a new algorithm designed to control the shape of the output probability density function (PDF) of singular systems subjected to non-Gaussian input. The aim is to select a control input uk such that the output PDF is made as close as possible to a given PDF. Based on the B-spline neural network approximation of the output PDF, the control algorithm is formulated by extending the developed PDF control strategies of non-singular systems to singular systems. It has been shown that under certain conditions the stability of the closed-loop system can be guaranteed. Simulation examples are given to show the effectiveness of the proposed control algorithm.
| Original language | English |
|---|---|
| Pages (from-to) | 151-160 |
| Number of pages | 9 |
| Journal | Zidonghua Xuebao/Acta Automatica Sinica |
| Volume | 31 |
| Issue number | 1 |
| Publication status | Published - Jan 2005 |
Keywords
- B-splines neural networks
- Dynamic stochastic systems
- Probability density function (PDF)
- Singular systems