Joint state and actuator fault estimation for networked systems under improved accumulation-based event-triggered mechanism

• Published in: ISA transactions Vol. 127; pp. 60 - 67
• Main Authors: Guo, Jiyue, Liu, Hongjian, Hu, Jun, Song, Baoye
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.08.2022
• Subjects: Actuator fault; Improved accumulation-based event-triggered mechanism (ETM); Joint estimation; Networked systems; Recursive matrix equations; Networked systems; Joint estimation; Recursive matrix equations; Improved accumulation-based event-triggered mechanism (ETM); Actuator fault
• ISSN: 0019-0578; 1879-2022; 1879-2022
• DOI: 10.1016/j.isatra.2022.04.011

The joint state and actuator fault estimation problem is investigated in this paper for a type of networked systems subject to loss of the actuator effectiveness (LAE). A so-called improved accumulation-based event-triggered mechanism (ETM) is used to regulate the transmission of signals between the sensors and the estimator for the purpose of communication resource saving. Compared with the traditional ETM schemes, such accumulation-based ETM is robust against the “undesired” abrupt changes of signals (which would occur due to certain big noises). Different from the integral-based ETM for continuous-time systems, the improved accumulation-based ETM proposed in this paper is a “weighted” ETM, where a given weight coefficient is employed to “balance” the weights of output measurements in different time instants. The multiplicative LAE is described by an unknown diagonal matrix. The object of this paper is to design a remote estimator such that both the fault signals and system states can be simultaneously estimated in the sense of minimizing an upper bound of the corresponding estimation error covariance at each sampling instant. First, the upper bound of the estimation error covariance is given by means of the induction method. Then, the desired estimator gain is calculated recursively by solving two sets of coupled matrix equations. Finally, two simulation examples are given to verify the usefulness of the strategy we proposed subject to the LAE under the improved accumulation-based ETM. •The joint state and actuator fault estimation problem is, for the first time, investigated for networked systems under the effects of accumulation-based ETM.•A novel improved accumulation-based ETM is proposed for discrete-time systems to reduce the signal transmissions while guaranteeing the desired estimation performance.