Abstract
This paper applies a quasi-ARMAX modeling technique, recently presented in the literature, to a process control framework. The use of this quasi-ARMAX modeling technique in nonlinear model predictive control (NMPC) formulations applied to simple nonlinear process control examples is investigated. The quasi-ARMAX predictor can be interpreted as a neuro-fuzzy predictor, and this neuro-fuzzy predictor is computationally straightforward and has showed excellent prediction capabilities. The predictor is thus well suited for NMPC purposes. Furthermore, the parameters of the neuro-fuzzy model can be argued to have explicit meaning, thus making the procedure of tuning the NMPC system more transparent when using the neuro-fuzzy predictor.
| Original language | English |
|---|---|
| Pages (from-to) | 39-44 |
| Number of pages | 6 |
| Journal | Research Reports on Information Science and Electrical Engineering of Kyushu University |
| Volume | 6 |
| Issue number | 1 |
| Publication status | Published - 2001 Dec 1 |
| Externally published | Yes |
Keywords
- Model predictive control
- Neuro fuzzy models
- Nonlinear control
- Nonlinear identification
ASJC Scopus subject areas
- Computer Science(all)
- Electrical and Electronic Engineering