A recurrent neural-network-based sensor and actuator fault detection and isolation for nonlinear systems with application to the satellite's attitude control subsystem

IEEE Transactions on Neural Networks

Volumn 20, Issue 1, 2009, Pages 45-60

  Talebi, H A ^a   Khorasani, K ^b   Tafazoli, S ^c  

^a AMIRKABIR UNIVERSITY OF TECHNOLOGY   (Iran)

^b Civil and Environmental Engineering   (Canada)

^c CANADIAN SPACE AGENCY   (Canada)

Author keywords

Attitude control subsystem (ACS); Dynamic neural networks; Fault detection and isolation (FDI); Nonlinear systems

Indexed keywords

ACTUATORS; ATTITUDE CONTROL; ELECTRIC FAULT LOCATION; FAULT DETECTION; NAVIGATION; NONLINEAR SYSTEMS; ORBITS; RECURRENT NEURAL NETWORKS; SENSOR NETWORKS; SENSORS; SPATIAL VARIABLES CONTROL; SYSTEM STABILITY;

ACTUATOR FAULT DETECTION; ACTUATOR FAULTS; ATTITUDE CONTROL SUBSYSTEM (ACS); ATTITUDE DETERMINATION (AD); CASE STUDIES; DYNAMIC NEURAL NETWORKS; EARTH ORBITS; FAULT DETECTION AND ISOLATION (FDI); FAULT DIAGNOSIS (FD); GENERAL CLASS; LYAPUNOV'S DIRECT METHOD; MAGNETORQUER; NETWORK WEIGHTS; SENSOR FAULTS; SENSOR NOISES; SIMULATION STUDIES; STABILITY ANALYSIS (IGC: E5/E13);

NEURAL NETWORKS;

ALGORITHM; ALTITUDE; ARTICLE; ARTIFICIAL NEURAL NETWORK; COMPUTER SIMULATION; ELECTROMAGNETIC FIELD; NONLINEAR SYSTEM; SPACE FLIGHT; VALIDATION STUDY;

ALGORITHMS; ALTITUDE; COMPUTER SIMULATION; ELECTROMAGNETIC FIELDS; NEURAL NETWORKS (COMPUTER); NONLINEAR DYNAMICS; SPACECRAFT;

EID: 58649091979     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2008.2004373     Document Type: Article

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