Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/154494
Title: PAC : A novel self-adaptive neuro-fuzzy controller for micro aerial vehicles
Authors: Ferdaus, Md Meftahul
Pratama, Mahardhika
Anavatti, Sreenatha G.
Garratt, Matthew A.
Lughofer, Edwin
Keywords: Engineering::Computer science and engineering
Issue Date: 2020
Source: Ferdaus, M. M., Pratama, M., Anavatti, S. G., Garratt, M. A. & Lughofer, E. (2020). PAC : A novel self-adaptive neuro-fuzzy controller for micro aerial vehicles. Information Sciences, 512, 481-505. https://dx.doi.org/10.1016/j.ins.2019.10.001
Project: RG130/17
Journal: Information Sciences
Abstract: 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 feed-forward 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.
URI: https://hdl.handle.net/10356/154494
ISSN: 0020-0255
DOI: 10.1016/j.ins.2019.10.001
Rights: © 2019 Elsevier Inc. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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