Abstract
This paper presents two robust solutions to the control of the output probability density function for multi-input and multi-output stochastic systems, where the purpose of control input design is to minimize the difference between the probability density function of the system output and a given one. The probability density function of the system output is approximated by a B-spline neural network with all its weights dynamically related to the control input. The measured probability density function of the system output is directly used to construct two robust control algorithms which are insensitive to the unknown input. The stability of the closed loop system are proved under certain conditions. An illustrative example is included to demonstrate use of the developed control algorithms and desired results have been obtained.
| Original language | English |
|---|---|
| Pages (from-to) | 1305-1310 |
| Number of pages | 6 |
| Journal | Proceedings of the IEEE Conference on Decision and Control |
| Volume | 2 |
| State | Published - 1998 |
| Externally published | Yes |
| Event | Proceedings of the 1998 37th IEEE Conference on Decision and Control (CDC) - Tampa, FL, USA Duration: Dec 16 1998 → Dec 18 1998 |