This paper presents a novel Minimum Resource Allocation Neural Network (MRAN) based controller that enhances the fault tolerance capabilities of a high performance fighter aircraft during the landing phase when subjected to severe winds and failures such as stuck control surfaces. The neural controller is trained on-line to learn the inverse dynamics of the aircraft. Autolanding simulations show that the fault-tolerance envelope of the combined MRAN+SMC+BTFC controller is much wider than those of the BTFC and BTFC+SMC controllers. It is assumed that information about actuator failures is not available to the controller for use in reconfiguration, and no Fault Detection and Diagnosis (FDD) schemes are used.
Guaranteed Stability and Improved Performance Against Actuator Failures Using Neural-aided Sliding Mode Controller for Autolanding Task
2012
6 Seiten, Bilder, Tabellen, Quellen
Aufsatz (Konferenz)
Englisch
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