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

Ucak K., Öke Günel G.

NEURAL PROCESSING LETTERS, cilt.53, sa.1, ss.405-428, 2021 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 53 Sayı: 1
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1007/s11063-020-10403-8
  • Dergi Adı: NEURAL PROCESSING LETTERS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, PASCAL, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, Information Science and Technology Abstracts, INSPEC, zbMATH, DIALNET
  • Sayfa Sayıları: ss.405-428
  • Anahtar Kelimeler: Model based adaptive control, NARMA-L2 SVR controller, NARMA-L2 SVR model, Online support vector regression, System identification
  • İstanbul Teknik Üniversitesi Adresli: Evet

Özet

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.