scholarly journals Improving 3D Path Tracking of Unmanned Aerial Vehicles through Optimization of Compensated PD and PID Controllers

2021 ◽  
Vol 12 (1) ◽  
pp. 99
Author(s):  
Nadia Samantha Zuñiga-Peña ◽  
Norberto Hernández-Romero ◽  
Juan Carlos Seck-Tuoh-Mora ◽  
Joselito Medina-Marin ◽  
Irving Barragan-Vite
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Dynamic Performance ◽  
Metaheuristic Algorithms ◽  
Pid Controllers ◽  
Aerial Vehicles ◽  
Wide Range ◽  
Time Parameters ◽  
Chaotic Nature ◽  
Complex Nonlinear Systems ◽  
Quadrotor Unmanned Aerial Vehicles

The development of quadrotor unmanned aerial vehicles (QUAVs) is a growing field due to their wide range of applications. QUAVs are complex nonlinear systems with a chaotic nature that require a controller with extended dynamics. PD and PID controllers can be successfully applied when the parameters are accurate. However, this parameterization process is complicated and time-consuming; most of the time, parameters are chosen by trial and error without guaranteeing good performance. The originality of this work is to present a novel nonlinear mathematical model with aerodynamic moments and forces in the Newton–Euler formulation, and identify metaheuristic algorithms applied to parameter optimization of compensated PD and PID controls for tracking the trajectories of a QUAV. Eight metaheuristic algorithms (PSO, GWO, HGS, LSHADE, LSPACMA, MPA, SMA and WOA) are reported, and RMSE is used to measure each dynamic performance of the simulations. For the PD control, the best performance is obtained with the HGS algorithm with an RMSE = 0.037247252379126. For the PID control, the best performance is obtained with the HGS algorithm with an RMSE = 0.032594309723623. Trajectory tracking was successful for the QUAV by minimizing the error between the desired and actual dynamics.

A Two-Step Optimization-Based Iterative Learning Control for Quadrotor Unmanned Aerial Vehicles

2021 ◽  
Vol 143 (7) ◽  
Author(s):  
Revant Adlakha ◽  
Minghui Zheng
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Design Problem ◽  
Iterative Learning Control ◽  
Design Method ◽  
Learning Control ◽  
Iterative Learning ◽  
Learning Performance ◽  
Aerial Vehicles ◽  
Quadrotor Unmanned Aerial Vehicles ◽  
Optimization Based Design

Abstract This paper presents a two-step optimization-based design method for iterative learning control and applies it onto the quadrotor unmanned aerial vehicles (UAVs) trajectory tracking problem. Iterative learning control aims to improve the tracking performance through learning from errors over iterations in repetitively operated systems. The tracking errors from previous iterations are injected into a learning filter and a robust filter to generate the learning signal. The design of the two filters usually involves nontrivial tuning work. This paper presents a new two-optimization design method for the iterative learning control, which is easy to obtain and implement. In particular, the learning filter design problem is transferred into a feedback controller design problem for a purposely constructed system, which is solved based on H-infinity optimal control theory thereafter. The robust filter is then obtained by solving an additional optimization to guarantee the learning convergence. Through the proposed design method, the learning performance is optimized and the system's stability is guaranteed. The proposed two-step optimization-based design method and the regarding iterative learning control algorithm are validated by both numerical and experimental studies.

scholarly journals Internet of Unmanned Aerial Vehicles—A Multilayer Low-Altitude Airspace Model for Distributed UAV Traffic Management

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4779 ◽  
Author(s):  
Nader S. Labib ◽  
Grégoire Danoy ◽  
Jedrzej Musial ◽  
Matthias R. Brust ◽  
Pascal Bouvry
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Traffic Management ◽  
Complex Problem ◽  
Experimental Simulation ◽  
Aerial Vehicles ◽  
Safe Management ◽  
Wide Range ◽  
Abstract Level ◽  
Low Altitude ◽  
New Research

The rapid adoption of Internet of Things (IoT) has encouraged the integration of new connected devices such as Unmanned Aerial Vehicles (UAVs) to the ubiquitous network. UAVs promise a pragmatic solution to the limitations of existing terrestrial IoT infrastructure as well as bring new means of delivering IoT services through a wide range of applications. Owning to their potential, UAVs are expected to soon dominate the low-altitude airspace over populated cities. This introduces new research challenges such as the safe management of UAVs operation under high traffic demands. This paper proposes a novel way of structuring the uncontrolled, low-altitude airspace, with the aim of addressing the complex problem of UAV traffic management at an abstract level. The work, hence, introduces a model of the airspace as a weighted multilayer network of nodes and airways and presents a set of experimental simulation results using three UAV traffic management heuristics.

scholarly journals A sectorial fuzzy consensus algorithm for the formation flight of multiple quadrotor unmanned aerial vehicles

2020 ◽  
Vol 12 ◽  
pp. 175682932097357
Author(s):  
E Javier Ollervides-Vazquez ◽  
Erik G Rojo-Rodriguez ◽  
Octavio Garcia-Salazar ◽  
Luis Amezquita-Brooks ◽  
Pedro Castillo ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Fuzzy Controller ◽  
Fuzzy Theory ◽  
Lyapunov Theory ◽  
Formation Flight ◽  
Consensus Algorithm ◽  
State Variables ◽  
Globally Asymptotically Stable ◽  
Aerial Vehicles ◽  
Quadrotor Unmanned Aerial Vehicles

This paper presents an algorithm based on fuzzy theory for the formation flight of the multi-quadrotors. For this purpose, the mathematical model of N-quadrotor unmanned aerial vehicles is presented using the Newton-Euler formulation. The strategy of the formation flight is based on a structure composed by a sectorial fuzzy controller and the linear systems whose state variables are the position and velocity of the ith quadrotor. The stability analysis is described as a generalized form for N-quadrotor unmanned aerial vehicles and it is based on the Lyapunov theory. This analysis demonstrates that the closed-loop system is globally asymptotically stable so that the quadrotors unmanned aerial vehicles reach the consensus. Numerical simulation demonstrates the robustness of the proposed scheme for the formation flight even in the presence of disturbances. Finally, experimental results show the feasibility of the proposed algorithm for the formation flight of multiple unmanned aerial vehicles.

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