Fuzzy-based fault-tolerant control of Micro Aerial Vehicles (MAV) — A preliminary study

2017IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM) Pub Date : 2017-12-01 DOI:10.1109/HNICEM.2017.8269562

Mark Lester F. Padilla, S. Lao, R. Baldovino, A. Bandala, E. Dadios

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引用次数: 3

Abstract

Unmanned Aerial Vehicles (UAV) has gained popularity in the past decades. This has been widely used throughout the world in the fields of military, surveillance, agriculture, and construction. One of the main problems in Micro Aerial Vehicles (MAV), typically smaller version of UAV, is its ability to detect and tolerate faults inside the system. In this paper, a Fault-Tolerant Control (FTC) will be developed using fuzzy logic and uses battery percentage and degree of ability to hover as the crisp inputs. The fuzzy logic will use five and three membership functions for the Battery Percentage and Degree of Ability to Hover respectively. The output of the controller will be the degree of ability to continue a certain mission. Further studies can include other constraints such as mapping efficiency where neural networks and deep learning can be associated. Thus, making a hybrid system.

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基于模糊的微型飞行器容错控制初探

在过去的几十年里，无人驾驶飞行器(UAV)得到了普及。这在世界范围内被广泛应用于军事、监视、农业和建筑领域。微型飞行器(MAV)是典型的小型无人机，其主要问题之一是其检测和容忍系统内部故障的能力。本文将使用模糊逻辑开发一种容错控制(FTC)，并使用电池百分比和悬停能力程度作为清晰输入。模糊逻辑将分别为电池百分比和悬停能力程度使用五个和三个隶属函数。控制器的输出将是继续执行某项任务的能力程度。进一步的研究可以包括其他约束，如映射效率，其中神经网络和深度学习可以相关联。因此，制造一个混合系统。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2017IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)

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