Development of c-means Clustering Based Adaptive Fuzzy Controller for A Flapping Wing Micro Air Vehicle

TL;DR

This paper presents a data-driven, adaptive fuzzy controller based on Fuzzy C-Means clustering for a nature-inspired flapping wing micro air vehicle, enhancing its maneuverability and disturbance rejection capabilities.

Contribution

It introduces a novel adaptive fuzzy control approach using Fuzzy C-Means clustering for a bio-inspired FW MAV, bypassing traditional system modeling.

Findings

The controller effectively manages altitude under environmental disturbances.

The clustering-based approach improves adaptability and robustness.

The model demonstrates advantages over traditional first-principle models.

Abstract

Advanced and accurate modelling of a Flapping Wing Micro Air Vehicle (FW MAV) and its control is one of the recent research topics related to the field of autonomous Unmanned Aerial Vehicles (UAVs). In this work, a four wing Natureinspired (NI) FW MAV is modeled and controlled inspiring by its advanced features like quick flight, vertical take-off and landing, hovering, and fast turn, and enhanced manoeuvrability when contrasted with comparable-sized fixed and rotary wing UAVs. The Fuzzy C-Means (FCM) clustering algorithm is utilized to demonstrate the NIFW MAV model, which has points of interest over first principle based modelling since it does not depend on the system dynamics, rather based on data and can incorporate various uncertainties like sensor error. The same clustering strategy is used to develop an adaptive fuzzy controller. The controller is then utilized to control the altitude of the NIFW MAV, that can adapt with environmental disturbances by tuning the antecedent and consequent parameters of the fuzzy system.