Safety-Critical Support Vector Regressor Controller for Nonlinear Systems

Kemal Uçak*, İlker Üstoğlu, Gülay Öke Günel

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

In this study, a novel safety-critical online support vector regressor (SVR) controller based on the system model estimated by a separate online SVR is proposed. The parameters of the controller are optimized using closed-loop margin notion proposed in Uçak and Günel (Soft Comput 20(7):2531–2556, 2016). The stability analysis of the closed-loop system has been actualised to design an architecture where operation is interrupted and safety is assured in case of instability. The SVR controller proposed in Uçak and Günel (2016) has been improved to a safety-critical structure by the addition of a failure diagnosis block which carries out Lyapunov stability analysis and detects failures when the overall system becomes unstable. The performance of the proposed method has been evaluated by simulations carried out on a process control system. The results show that the proposed safety-critical SVR controller attains good modelling and control performances and failures arising from instability can be successfully detected.

Original languageEnglish
Pages (from-to)419-440
Number of pages22
JournalNeural Processing Letters
Volume48
Issue number1
DOIs
Publication statusPublished - 1 Aug 2018

Bibliographical note

Publisher Copyright:
© 2017, Springer Science+Business Media, LLC.

Keywords

  • Model based adaptive control
  • Online support vector regression
  • SVR controller
  • SVR model identification
  • Safety-critical SVR controller
  • Stability analysis

Fingerprint

Dive into the research topics of 'Safety-Critical Support Vector Regressor Controller for Nonlinear Systems'. Together they form a unique fingerprint.

Cite this