Guaranteed Stability and Improved Performance Against Actuator Failures Using Neural-aided Sliding Mode Controller for Autolanding Task

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.