Abstract
NARMA model is a simple and effective way to represent nonlinear systems, based on the NARMA model, NARMA-L2 controller is designed and has been successfully applied in the literature. Success of NARMA-L2 controller is directly related to the precision with which controlled systems’ dynamics can be estimated. In this paper, online SVR is utilized to obtain controlled plant’s subdynamics and consequently this information is used in the construction of NARMA-L2 controller. Hence functionality of NARMA-L2 controllers and high generalization capability of SVR are combined. Also, SVR formulates a convex optimization problem and therefore guarantees global optimum solution. The proposed method is assessed by performing simulations on a nonlinear CSTR system, the robustness of the designed controller is also tested under noisy and uncertainty conditions.
| Original language | English |
|---|---|
| Pages (from-to) | 405-428 |
| Number of pages | 24 |
| Journal | Neural Processing Letters |
| Volume | 53 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Feb 2021 |
Bibliographical note
Publisher Copyright:© 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature.
Keywords
- Model based adaptive control
- NARMA-L2 SVR controller
- NARMA-L2 SVR model
- Online support vector regression
- System identification