Adaptive neural-network-based distributed fault estimation for heterogeneous multi-agent systems

DOI10.1016/J.JFRANKLIN.2022.09.003zbMath1501.93078OpenAlexW4296260701MaRDI QIDQ2095016

Yanyan Li

Publication date: 9 November 2022

Published in: Journal of the Franklin Institute (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jfranklin.2022.09.003

zbMATH Keywords

heterogeneous multi-agent systemsdistributed fault estimationdaptive neural-network-based distributed fault estimationunmanned ground and aerial vehicles

Mathematics Subject Classification ID

Adaptive control/observation systems (93C40) Automated systems (robots, etc.) in control theory (93C85) Multi-agent systems (93A16)

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Cites Work

• Observer-based distributed fault detection and isolation for second-order multi-agent systems using relative information
• Online reinforcement learning multiplayer non-zero sum games of continuous-time Markov jump linear systems
• A Novel Approach to State and Unknown Input Estimation for Takagi–Sugeno Fuzzy Models With Applications to Fault Detection
• Distributed Optimal Fault Estimation and Fault-Tolerant Control for Interconnected Systems: A Stackelberg Differential Graphical Game Approach
• On Unknown Input Observers of Linear Systems: Asymptotic Unknown Input Decoupling Approach
• Distributed formation output regulation of switching heterogeneous multi-agent systems
• Distributed Time-Varying Output Formation Tracking for Heterogeneous Linear Multiagent Systems With a Nonautonomous Leader of Unknown Input
• Hierarchical-Structure-Based Fault Estimation and Fault-Tolerant Control for Multiagent Systems
• Distributed hierarchical control design of coupled heterogeneous linear systems under switching networks
• Adaptive optimization algorithm for nonlinear Markov jump systems with partial unknown dynamics
• Input-to-state stability of impulsive reaction-diffusion neural networks with infinite distributed delays

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