Medium-Scale UAVs: A Practical Control System Considering Aerodynamics Analysis

Abstract

Unmanned aerial vehicles (UAVs) have drawn significant attention from researchers over the last decade due to their wide range of possible uses. Carrying massive payloads concurrent with light UAVs has broadened the aeronautics context, which is feasible using powerful engines; however, it faces several practical control dilemmas. This paper introduces a medium-scale hexacopter, called the Fan Hopper, alimenting Electric Ducted Fan (EDF) engines to investigate the optimum control possibilities for a fully autonomous mission carrying a heavy payload, even of liquid materials, considering calculations of higher orders. Conducting proper aerodynamic simulations, the model is designed, developed, and tested through robotic Gazebo simulation software to ensure proper functionality. Correspondingly, an Ardupilot open source autopilot is employed and enhanced by a model reference adaptive controller (MRAC) for the attitude loop to stabilize the system in case of an EDF failure and adapt the system coefficients when the fluid payload is released. Obtained results reveal less than a 5% error in comparison to desired values. This research reveals that tuned EDFs function dramatically for large payloads; meanwhile, thermal engines could be substituted to maintain much more flight endurance.

A Proposed System for Multi-UAVs in Remote Sensing Operations

“…Once the decision-making system defines the waypoints, the reference values of the controller are desired, which are the position and heading angles. Secondly, the attitude desired values are generated, and finally, the motor mixing part interprets the commands for the motors (see [ 22 , 23 , 24 ]).…”

“…Solving for the Laplacian transformation for a time-varying output model, as mentioned in [ 6 , 7 ], gives: where L and are the Laplacian transformation and transform inverse, respectively; the term is a standard remarking for Laplacian transformation matrix; and is the Laplacian form of , in which both and a are positive constants equal to or bigger than the transformation matrix power. Following the solution performed in previous works [ 22 , 23 , 24 ], controller update parameters that update the system’s error ( ) matrix could be defined as . Therefore, the state space formulas can be written as follows: …”

A Nonlinear Adaptive Autopilot for Unmanned Aerial Vehicles Based on the Extension of Regression Matrix

“…Various effective control methods have been applied to control UAV flight, which have delivered good performance. Mohammad utilized model reference adaptive control (MRAC) to track the attitude of medium-scale UAVs [2]. Karim Ahmadi conducted applications of nonlinear dynamic inversion control to quad-rotors [3], and L. A. Blas achieved similar applications with active disturbance rejection control [4].…”

Thrust Vectoring Control for Heavy UAVs, Employing a Redundant Communication System

“…Nowadays, electrical UAVs mostly work accurately and have been improved a dozen times; however, they suffer from low flight endurance, and if electrical motors are substituted by thermal engines, the controller surfaces would be changed due to the limits of thermal systems. The authors have previously discussed the limitations and solutions proposed in [ 11 , 12 ]. In this paper, a novel approach is put forth that, despite its rarity in the history of aeronautics, if constructed properly, may stabilize an unmanned aerial vehicle (UAV) for a long period, even in the presence of wind disturbances.…”

“…In this paper, a novel approach is put forth that, despite its rarity in the history of aeronautics, if constructed properly, may stabilize an unmanned aerial vehicle (UAV) for a long period, even in the presence of wind disturbances. More literature reviews could be found in authors’ previous works [ 11 , 12 , 13 , 14 , 15 , 16 ]. Briefly, the control strategy presented in this paper—thrust vectoring using flap vanes—offers various advantages over other strategies, including simplicity of servo installation and reduction in the mechanical complexities compared to those of collective pitch propellers because they have fewer movable components; more dynamic stability in attitude control, rather than rotatable hinges or ducts; more efficiency in lift generation based on flap design, as opposed to collective pitch props, which also offer faster response times, and when adjusting the flow direction and magnitude, allows for rapid changes in thrust vectoring, which can be advantageous for applications requiring agile flight control [ 17 ]; and finally, properly designed flap vanes can contribute to noise reduction.…”