A Novel Adaptive NARMA-L2 Controller Based on Online Support Vector Regression for Nonlinear Systems

Ucak K., GUNEL G. O.

NEURAL PROCESSING LETTERS, vol.44, no.3, pp.857-886, 2016 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 44 Issue: 3
  • Publication Date: 2016
  • Doi Number: 10.1007/s11063-016-9500-7
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.857-886
  • Istanbul Technical University Affiliated: No


In this study, a novel nonlinear autoregressive moving average (NARMA)-L2 controller based on online support vector regression (SVR) is proposed. The main idea is to obtain a SVR based NARMA-L2 model of a nonlinear single input single output system (SISO) by decomposing a single SVR which estimates the nonlinear autoregressive with exogenous inputs (NARX) model of the system. Consequently, using the obtained SVR-NARMA-L2 submodels, a NARMA-L2 controller is designed. The performance of the proposed SVR based NARMA-L2 controller has been evaluated by simulations carried out on a bioreactor system, and the results show that the SVR based NARMA-L2 model and controller attain good modelling and control performances. Robustness of the controller in the case of system parameter uncertainty and measurement noise have also been examined.