Safety-Critical Support Vector Regressor Controller for Nonlinear Systems - Ucak, Kemal; Ustoglu, Ilker; Gunel, Gulay Oke (SPRINGER, 2018)
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 U double dagger ak and Gunel (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 U double dagger ak and Gunel (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.
Keywords: Model based adaptive control; Online support vector regression; Safety-critical SVR controller; SVR controller; SVR model identification; Stability analysis; GENERALIZED PREDICTIVE CONTROL; ADAPTIVE INVERSE CONTROL; MACHINES; NETWORKS; CLASSIFICATION; Computer Science