A comparative study on the control of quadcopter UAVs by using singleton and non-singleton fuzzy logic controllers

Abstract

Abstract-Fuzzy logic controllers (FLCs) have extensively been used for the autonomous control and guidance of unmanned aerial vehicles (UAVs) due to their capability of handling uncertainties and delivering adequate control without the need for a precise, mathematical system model which is often either unavailable or highly costly to develop. Despite the fact that non-singleton FLCs (NSFLCs) have shown more promising performance in several applications when compared to their singleton counterparts (SFLCs), most of UAV applications are still realized by using SFLCs. In this paper, we explore the potential of both standard and the recently introduced centroid based NSFLCs, i.e., Sta-NSFLC and Cen-NSFLC, for the control of a quadcopter UAV under various input noise conditions using different levels of fuzzifier, and a comparative study has been conducted using the three aforementioned FLCs. We present a series of simulation-based experiments, the simulation results show that the control performances of NSFLCs are better than those of SFLC, and the Cen-NSFLC outperforms the Sta-NSFLC especially under highly noisy conditions.

Similarity-based non-singleton fuzzy logic control for improved performance in UAVs

“…The aforementioned formulations show that the firing level of an antecedent is its membership degree at the centroid of the intersection with the input FS. Although the Cen-NSFLC outperformed the Sta-NSFLC for the autonomous control and stabilization of the UAVs in our previous work [13]. Based on [15], we hypothesize that the performance of the NSFLCs can be further improved in our UAV tests by replacing the composition-based inference engine with the similarity-based inference engine as outlined next.…”

“…And each element in this vector is fuzzified with a Gaussian distribution. In the literature, NSFLCs which have been used for controlling UAVs, can be generally divided into two types based on different composition-based inference engines [13]: (I) the NSFLC with standard composition-based inference engine [12], i.e., Sta-NSFLC and (II) the NSFLC with centroid composition-based inference engine [14], i.e., Cen-NSFLC. In the Cen-NSFLC, the centroid of the FS intersection between the input and antecedent FSs is used for calculating the firing strength of each rule rather than the maximum of the intersection utilized in Sta-NSFLCs.…”

“…To make the NSFLC more sensitive to the input uncertainty, the Cen-NSFLCs have been presented to control and stabilize the UAVs recently [13]. Taking the derivative of error de for example (as the blue rectangle shown in the Fig.…”

“…Therefore, non-singleton FLCs (NSFLCs) are preferred as they can deal with the uncertainties by modeling the inputs as input fuzzy sets (FSs) [12]. In our previous paper [13], we investigated that applying two NSFLCs with standard and a novel centroid-based inference engines, i.e., Sta-NSFLC [12] and Cen-NSFLC [14], to control and stabilize the UAVs. The UAV flight results show that the Cen-NSFLCs can achieve better control performance than the Sta-NSFLCs.…”

Novel Levenberg–Marquardt based learning algorithm for unmanned aerial vehicles

“…Therefore, fuzzy logic controllers (FLCs) have become one of the most popular approaches to control nonlinear systems when their precise mathematical model is challenging to obtain (Castillo et al, 2016a;Cervantes and Castillo, 2015;Mendel et al, 2014). FLCs have been successfully designed and implemented to control mobile robots (Castillo et al, 2016b;Tai et al, 2016;Sanchez et al, 2015;Kumbasar and Hagras, 2014;Hagras, 2004), especially UAVs (Fu et al, 2016;Fakurian et al, 2014). However, one weakness of FLCs is that they need to be tuned to deal with uncertainties.…”