Name
Abstract | ||
|---|---|---|
The appropriately tuned parameters allow a successful implementation of Model Predictive Control (MPC). In this paper an Artificial-Neural-Network (ANN) based approach is presented and detailed in the case of second order Single-Input-Single-output (SISO) system with active constraints. The benefits of our novel proposed approach lie in its capability to reach closed-loop stability and tune online the MPC parameters using Particle-Swarm-Optimization (PSO), and Online-Sequential-Extreme-Learning-Machine(OS-ELM). Finally, the effectiveness of our approach has been emphasized by comparing the obtained performances to other existing methods. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICCA.2019.8900026 | 2019 IEEE 15TH INTERNATIONAL CONFERENCE ON CONTROL AND AUTOMATION (ICCA) |
Keywords | Field | DocType |
Model predictive control, tuning parameters, Artificial Neural Network based approach, SISO, PSO, OS-ELM | Control theory,Model predictive control,Control engineering,Engineering,Artificial neural network | Conference |
ISSN | Citations | PageRank |
1948-3449 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Houssam Moumouh | 1 | 0 | 0.34 |
| Nicolas Langlois | 2 | 26 | 12.61 |
| Madjid Haddad | 3 | 0 | 1.01 |