RESUMEN
This paper proposes a data-driven actuator fault detection and isolation approach for the general class of nonlinear systems. The proposed method uses a deep neural network architecture to obtain an invariant set of basis functions for the Koopman operator to form a linear Koopman predictor for a nonlinear system. Then, the obtained linear model is used for fault detection and isolation purposes without relying on prior knowledge about the underlying dynamics. Moreover, a recursive method is proposed for fault detection and isolation that is entirely data-driven with the key feature of global validity for the system's whole operating region due to the Koopman operator's global characteristic. Finally, the approach's efficacy is demonstrated using two simulations on a coupled nonlinear system and a two-link manipulator benchmark.
RESUMEN
In this paper, a new tracking control structure is proposed to decrease the time-delay effect of tracking sensor. To achieve this purpose, an angular position sensor, which generally exists in tracking systems, is used together with the tracking sensor. Also, a compensator is designed and applied to a system with time-delay in order to obtain a behavior same as a system without time-delay. Relying only on tracking sensor may lead to reduce the tracking speed and to increase tracking error. However, it is shown that by using the proposed reformative structure, the speed of tracking and the tracking error can be compensated significantly. In the next step, the performance of the new structure in two cases of constant time-delay and variable time-delay are evaluated and their stability conditions are analyzed. Finally, robustness of the proposed structure is analyzed.