Adaptive fuzzy fault-tolerant controller for aircraft autolanding under failures

This paper presents an adaptive fuzzy control strategy for an aircraft automatic landing problem under the failures of stuck control surfaces and severe winds. The strategy incorporates a dynamic fuzzy system called sequential adaptive fuzzy inference system (SAFIS) and it augments an existing conventional controller called baseline trajectory following controller (BTFC). SAFIS is an online learning fuzzy system in which the rules are added or deleted based on the input data. Also, SAFIS incorporates an online scheme for parameter update of the membership functions. BTFC has been designed using classical control methods under normal operating conditions with winds. For this study, the following fault scenarios have been considered: 1) single fault of either aileron or elevator stuck at certain deflections, and 2) double fault cases where one aileron and one elevator at the same or opposite direction are stuck at different deflections. Simulation studies indicate that the BTFC is unable to handle these failures. Recently, Abhay et al. have proposed a neural-based scheme to augment the BTFC and its performance has been shown to be superior. However, even in this neural scheme there are gaps in the fault cases where performance specifications are not met. In this paper, results show that the SAFIS-aided BTFC improves the fault-tolerant capabilities compared with BTFC and also the earlier neural-aided BTFC performance in filling up the gaps observed earlier.