Logo image
Co-Design of adaptive event generator and asynchronous fault detection filter for Markov jump systems via genetic algorithm
Journal article   Peer reviewed

Co-Design of adaptive event generator and asynchronous fault detection filter for Markov jump systems via genetic algorithm

X. Zhang, H. Wang, J. Song, S. He and C. Sun
IEEE Transactions on Cybernetics, Early Access
2022
url
Link to Published Version *Subscription may be requiredView

Abstract

This article investigates the co-design problem of adaptive event-triggered schemes (AETSs) and asynchronous fault detection filter (AFDF) for nonhomogeneous higher-level Markov jump systems, involving the hidden Markov model (HMM), higher-level Markov chain (MC), and conic-type nonlinearities. The transformation of the system transition probability can be reflected by the designed higher-level MC. An HMM with another conditional transition probability is applied to detect higher-level Markov processes and make the system be more practical. In order to balance the utilization of network resources and system performance, a novel AETS is proposed and used in the construction of the AFDF. By the Lyapunov theory, sufficient conditions are given to ensure the existences of the AETS and AFDF. It is not only an appropriate tradeoff between the utilization of network resources and system performance, but also reduces the conservatism. Finally, a numerical example is given to detect the faults effectively by the co-designed AFDF.

Details

Metrics

InCites Highlights

These are selected metrics from InCites Benchmarking & Analytics tool, related to this output

Collaboration types
Domestic collaboration
International collaboration
Citation topics
4 Electrical Engineering, Electronics & Computer Science
4.29 Automation & Control Systems
4.29.30 Robust Control Systems
Web Of Science research areas
Automation & Control Systems
Computer Science, Artificial Intelligence
Computer Science, Cybernetics
ESI research areas
Computer Science
Logo image