Akademik Veri Yönetim Sistemi
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, cilt.81, ss.47-67, 2019 (SCI-Expanded)
In this study, a novel model free support vector regressor controller (MF-SVRcontroller) 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-SVRcontroller. The effectiveness of the adjustment mechanism and closed-loop performance of the MF-SVRcontroller 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-SVRcontroller is compared with a model based support vector regressor controller (MB-SVRcontroller) and SVM-based PID controller. The results indicate that MF-SVRcontroller diminishes the computational load of MB -SVRcontroller at the cost of a small amount of decrease in tracking performance.