Control Algorithms for UAVs: A Comprehensive Survey

Nguyen, Ha Q.; Quyen, Toan V.; Nguyen, Cuong V.; Le, Anh; Tran, Hoa; Nguyen, Minh Tuan

doi:10.4108/eai.18-5-2020.164586

Cited by 34 publications

(18 citation statements)

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“…It can be seen that a single nonlinear controller has some limitations in the UAV control process. To overcome this problem, there are researches that combine the advantages of two or more algorithms together to improve the performance of the controller [13].…”

Section: Model Predictive Control (Mpc) Model Predictive Control (Mpc...mentioning

confidence: 99%

Classification of Modern Unmanned Aerial Vehicles

Tachinina

Lysenko

Kutiepov

2022

ECS

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The article examines the issue of classification of unmanned aerial vehicles. The existing classifications of unmanned aerial vehicles are considered, as well as the characteristics by which unmanned aerial vehicles are classified. Like any aircraft, unmanned aerial vehicles can be classified by general characteristics such as type of aircraft, size, weight, engine type, degree of autonomy, application, and other characteristics. Due to dynamic development of the field of unmanned aerial vehicles, the number of their classification characteristics is increasing. Due to the rapid development of unmanned aerial vehicles, existing classifications of unmanned aerial vehicles do not consider some existing types of unmanned aerial vehicles. The main goal is to propose a classification of unmanned aerial vehicles, which takes into account great variety of modern unmanned aerial vehicles. The following characteristics were considered, according to which unmanned aerial vehicles are classified, namely - by type of aircraft, by size, by range and endurance, by degree of autonomy, by altitude, by engine type, by take-off and landing principle, by application. As a result of conducted analysis of characteristics by which unmanned aerial vehicles are classified, a classification of unmanned aerial vehicles is proposed based on their characteristics, designs and tasks they perform.

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Section: Model Predictive Control (Mpc) Model Predictive Control (Mpc...mentioning

confidence: 99%

Classification of Modern Unmanned Aerial Vehicles

Tachinina

Lysenko

Kutiepov

2022

ECS

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“…Therefore, different control variables could be modified in order to adapt to control requirements. These types of UAVs can be classified by the form of wing function [15]. A flight control system of a single UAV consists of two stages.…”

Section: Problem Of Formation Controlmentioning

confidence: 99%

Formation Control Algorithms for Multiple-UAVs: A Comprehensive Survey

T.¹,

Hua²,

Nguyen³

et al. 2021

EAI Endorsed Transactions on Industrial Networks and Intelligen

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Unmanned aerial vehicles (UAVs) have been widely deployed in many applications such as transportation, data collection, monitoring, or tracking objects. Nowadays, numerous missions require UAVs to operate in a large area or to complete missions in a stringent period of time. Using a single UAV may not meet the performance requirements because of its small size and limited battery. In this situation, multiple Unmanned Aerial Vehicles (UAVs) have emerged as an effective measure that can address these limitations. A group of UAVs cooperatively working together could offer a solution that is more efficient and economical than using a powerful UAV alone. To better utilizing the multiple-UAVs system, control of formation UAVs is a critical challenge that needs to overcome. Therefore, formation control has become an active research topic that gains great attention from researchers. Extensive research efforts have been dedicated to studying the formation control problem with numerous control protocols which have been proposed. This paper reviews the profound studies on formation control in literature. Each approach is investigated based on different criteria, which highlights its distinct merits and demerits. The comparison is provided to facilitate the readers in their future researches in the field of formation control. Finally, some open challenges and research directions are also discussed.

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“…PID has many advantages as simply an algorithm and guarantees satisfactory performance. The limitation of PID, the value of control gain (Kp, Ki, Kd), constantly leads to reduced control quality in the vast range of operations, especially in nonlinear under-actuated systems [3]. LQR is a type of linear optimal control; its quality depends on the accuracy of the system model and the value of the weight matrices Q and R.…”

Section: Introductionmentioning

confidence: 99%