Adaptive data-driven fault-tolerant control for nonlinear systems: Koopman-based virtual actuator approach

Abstract

This paper proposes an adaptive data-driven fault-tolerant control scheme using the Koopman operator for unknown dynamics subjected to nonlinearities, time-varying loss of effectiveness, and additive actuator faults. The main objective of this method is to design a virtual actuator to hide actuator faults from the view of the system's nominal controller without having any prior knowledge about the system's underlying dynamics. The designed virtual actuator is placed between the faulty plant and the nominal controller of the system to keep the dynamical system's performance consistent before and after the occurrence of actuator faults. Based on the Koopman operator theory, an equivalent Koopman predictor is first obtained using the process data only, without knowing the governing equations of the underlying dynamics. Koopman operator is an infinite-dimensional, linear operator which takes the nonlinear process data into an infinite-dimensional feature space where the dynamic data correlations have linear behavior. Next, based on the approximated system's Koopman operator, a virtual actuator is designed and implemented without knowing the system's nominal controller. Needless to use a separate fault detection, isolation, and identification module to perform fault-tolerant control, the current method leverages the adaptive framework to keep the system's desired performance in facing time-varying additive and loss of effectiveness actuator faults. Finally, the approach's efficacy is demonstrated using simulation on a two-link manipulator benchmark, and a comparison study is presented.