Multiple Model-Based Fault Detection and Diagnosis for Nonlinear Model Predictive Fault-Tolerant Control

Abstract

This paper presents a novel fault-tolerant control system for a class of nonlinear systems with input constraints. A fault detection and diagnosis (FDD) is designed based on multiple model method. The bank of extended Kalman filters is used to detect the predefined actuator fault and estimate the unknown parameters of actuator position. On the other hand, until the fault detection instance, because of the mismatch between the process and the model, the system states may exit the stability region. Therefore, delay on FDD decision may lead to performance degradation or even instability for some systems. The timely proposed FDD approach could preserve system stability. When the fault is detected, the proposed FDD information is used to correct the model of faulty system recursively and reconfigure the controller. On the other hand, because of many important factors of MPC such as consideration of input and state constraints in optimization problem, it can be used as a powerful controller in the event of fault occurrence. So, the reconfigurable controller is designed based on the Lyapunov-based model predictive control approach that provides an explicit characterization of the stability region. Finally, a practical chemical process example, is presented to illustrate the effectiveness of this idea. It is shown that this scheme can provide the system stability when a fault occurs.