Abstract
In this study, a novel model free support vector regressor controller (MF-SVR controller ) is introduced for nonlinear dynamical systems. For the adaptation mechanism, a model free closed-loop margin which is a function of tracking error is derived and it is used to optimize the parameters of MF-SVR controller . The effectiveness of the adjustment mechanism and closed-loop performance of the MF-SVR controller have been examined by simulations performed on continuously stirred tank reactor (CSTR) and bioreactor benchmark systems. In order to observe the impacts of the removal of the model estimation block in control architecture, the performance of the MF-SVR controller is compared with a model based support vector regressor controller (MB-SVR controller ) and SVM-based PID controller. The results indicate that MF-SVR controller diminishes the computational load of MB-SVR controller at the cost of a small amount of decrease in tracking performance.
Original language | English |
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Pages (from-to) | 47-67 |
Number of pages | 21 |
Journal | Engineering Applications of Artificial Intelligence |
Volume | 81 |
DOIs | |
Publication status | Published - May 2019 |
Bibliographical note
Publisher Copyright:© 2019 Elsevier Ltd
Keywords
- Adaptive control
- Direct adaptive control
- Model free adaptive control(MFAC)
- Model free SVR controller
- Online support vector regression
- SVR controller