An active fault-tolerant control method based on moving window hidden Markov model

This article proposes a new active fault-tolerant control (AFTC) method based on moving window hidden Markov models (MWHMM) for nonlinear processes. The AFTC approach could be divided into two steps, fault detection and identification (FDI) and fault accommodation. The information obtained from the FDI step is used for fault accommodation, under the framework of model predictive control (MPC). Firstly, MWHMM is utilized for online fault detection and identification. Moreover, to improve the discernibility of the prediction status probability density function (PDF) for identification between the normal and faulty modes, the Hellinger distance, which could measure the similarity between probability distributions, is introduced. As the separability between the normal and faulty modes is increased, the accuracy and reliability of fault identification by MWHMM is greatly improved. At last, not only the occurrence of a single fault but also the occurrence of a hybrid fault, which means that multiple faults occur simultaneously, is considered in this article. The validity of the suggested method is verified by the simulation results on a three-tank water system. (C) 2020 Elsevier Ltd. All rights reserved.