Abstract
In this paper we present an identification model constructed by static feedforward neural networks and stable filters for nonlinear dynamical systems. Adaptive identification and control schemes based on neural networks are shown to guarantee stability of the system, even in the presence of neural network approximation errors. Finally, sliding control is used to compensate for inherent network approximation errors in order to improve the tracking performance.
| Original language | English |
|---|---|
| Journal | Kongzhi Lilun Yu Yinyong/Control Theory and Applications |
| Volume | 12 |
| Issue number | 2 |
| Publication status | Published - 1995 |
| Externally published | Yes |
Keywords
- Identification
- Neural networks
- Nonlinear systems
- Sliding control