Type-2 fuzzy logic aggregation of multiple fuzzy controllers for airplane flight control

Cervantes, Leticia; Castillo, Oscar

doi:10.1016/j.ins.2015.06.047

Cited by 180 publications

(50 citation statements)

References 40 publications

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“…The significance of the ith rule is determined by its average activation degree for all possible incoming data samples or its expected values as expressed in Eq. (9). Considering the normal distribution assumption, the importance of ith rule can be expressed as HS i = ω i µ e .…”

Section: New Rule Pruning Module Of Pacmentioning

confidence: 99%

PAC: A novel self-adaptive neuro-fuzzy controller for micro aerial vehicles

Ferdaus

Pratama

Anavatti

et al. 2020

Information Sciences

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There exists an increasing demand for a flexible and computationally efficient controller for micro aerial vehicles (MAVs) due to a high degree of environmental perturbations. In this work, an evolving neuro-fuzzy controller, namely Parsimonious Controller (PAC) is proposed. It features fewer network parameters than conventional approaches due to the absence of rule premise parameters. PAC is built upon a recently developed evolving neuro-fuzzy system known as parsimonious learning machine (PALM) and adopts new rule growing and pruning modules derived from the approximation of bias and variance. These rule adaptation methods have no reliance on user-defined thresholds, thereby increasing the PAC's autonomy for real-time deployment. PAC adapts the consequent parameters with the sliding mode control (SMC) theory in the single-pass fashion. The boundedness and convergence of the closed-loop control system's tracking error and the controller's consequent parameters are confirmed by utilizing the LaSalle-Yoshizawa theorem. Lastly, the controller's efficacy is evaluated by observing various trajectory tracking performance from a bio-inspired flapping wing micro aerial vehicle (BI-FWMAV) and a rotary wing micro aerial vehicle called hexacopter. Furthermore, it is compared to three distinctive controllers. Our PAC outperforms the linear PID controller and feedforward neural network (FFNN) based nonlinear adaptive controller. Compared to its predecessor, G-controller, the tracking accuracy is comparable, but the PAC incurs significantly fewer parameters to attain similar or better performance than the G-controller.

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Section: New Rule Pruning Module Of Pacmentioning

confidence: 99%

PAC: A novel self-adaptive neuro-fuzzy controller for micro aerial vehicles

Ferdaus

Pratama

Anavatti

et al. 2020

Information Sciences

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“…The fuzzy logic has an exceptional ability to handle the uncertainties in the system (Celikyilmaz and Turksen, 2009). 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).…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Novel Levenberg–Marquardt based learning algorithm for unmanned aerial vehicles

Sarabakha¹,

Imanberdiyev²,

Kayacan

et al. 2017

Information Sciences

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In this paper, a Levenberg-Marquardt inspired sliding mode control theory based adaptation laws are proposed to train an intelligent fuzzy neural network controller for a quadrotor aircraft. The proposed controller is used to control and stabilize a quadrotor unmanned aerial vehicle in the presence of periodic wind gust. A proportional-derivative controller is firstly introduced based on which fuzzy neural network is able to learn the quadrotor's control model on-line. The proposed design allows handling uncertainties and lack of modelling at a computationally inexpensive cost. The parameter update rules of the learning algorithms are derived based on a Levenberg-Marquardt inspired approach, and the proof of the stability of two proposed control laws are verified by using the Lyapunov stability theory. In order to evaluate the performance of the proposed controllers extensive simulations and real-time experiments are conducted. The 3D trajectory tracking problem for a quadrotor is considered in the presence of time-varying wind conditions.

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“…Type-2 fuzzy sets are capable of handling situations in which there is uncertainty about Neural Comput & Applic the membership degrees themselves. In other words, these sets are useful in situations that are so fuzzy, in which determining the membership degree even as a crisp number in [0, 1] is not easily and efficiently done [13,24]. In this section, a brief presentation of the basic concepts and operations of interval type-2 fuzzy sets are presented [17,18,35,45,63].…”

Section: Preliminarymentioning

confidence: 99%

R&D project evaluation and project portfolio selection by a new interval type-2 fuzzy optimization approach

Mohagheghi

Mousavi

Vahdani

et al. 2016

Neural Comput & Applic

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In today's ever-changing and highly uncertain environment, organizations depend on research and development (R&D) activities to adapt intensive growth of technology. One of the most important and trickiest tasks of any developing firms is to define new projects. This process gets difficult when it comes to choosing an appropriate portfolio from a set of candidate projects. Since organizations are faced with limited resources of R&D and budget constraints, they have to choose a project portfolio that mitigates the corresponding risk and enhances the overall value of portfolio. Therefore, the purpose of this study is to introduce a practical model to select the best and the most proper project portfolio while considering project investment capital, return rate, and risk. The ever-changing and highly uncertain environment of projects is addressed by utilizing interval type-2 fuzzy sets (IT2FSs). In this paper, a new model of R&D project evaluation is first introduced. This model includes a new risk-return index. This model is then extended in project portfolio selection, and as a result, a new model of R&D project portfolio selection is proposed under uncertainty. Constraints and limitations of R&D project portfolio selection are comprehensively addressed. In this model, lower semi-variance is applied to consider risk of proposed projects. Therefore, this paper offers a new model that applies IT2FSs to handle uncertainty, uses semi-variance to assess risk, synchronously considers risk and return in its selection process, and addresses the considerations and limits of realworld problems. Eventually, to verify the proposed model, a numerical example of the existing literature is solved with the model, and the results are compared. The first proposed model is used to prioritize proposed R&D projects of a gas and oil development holding firm as a real case study. To illustrate further, a practical example is also provided to demonstrate the applicability of the proposed project portfolio selection model.

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Type-2 fuzzy logic aggregation of multiple fuzzy controllers for airplane flight control

Cited by 180 publications

References 40 publications

PAC: A novel self-adaptive neuro-fuzzy controller for micro aerial vehicles

PAC: A novel self-adaptive neuro-fuzzy controller for micro aerial vehicles

Novel Levenberg–Marquardt based learning algorithm for unmanned aerial vehicles

R&D project evaluation and project portfolio selection by a new interval type-2 fuzzy optimization approach

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