Modeling and control approach to a distinctive quadrotor helicopter

Wu, Jun; Peng, Hui; Chen, Qing; Peng, Xiaoyan

doi:10.1016/j.isatra.2013.08.010

Cited by 52 publications

(16 citation statements)

References 31 publications

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“…In Wu et al's study [49], RBF-ARX model trained with the least square method was proposed to represent the nonlinear dynamics of the quadcopter, and the results were compared with the physical model-based method. The quadrotor used in this study [49] has different configurations with the conventional quadcopter to reduce control complexity and avoid system damage. It has three propellers positioning horizontal to provide pitch and roll motion, and one propeller is mounted in vertical to control yaw motion.…”

Section: Fig 12-basic Rbf Nn Structurementioning

confidence: 99%

Neural Network-Based System Identification for Quadcopter Dynamic Modeling: A Review

Pairan¹,

Shamsudin²,

Zulkafli³

2020

JAMEA

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A quadcopter is a rotorcraft with a simple mechanical construction. It has the same hovering capability similar to the traditional helicopter, but it is easier to maintain. The quadcopter is hard to control due to its unstable system with highly coupled and non-linear dynamics. In order to design a robust control algorithm, it is crucial to obtain a precise quadrotor flight dynamics through system identification approach. System identification is a method of finding the mathematical model of the dynamics system using the input-output data measurement. Neural network (NN) based system identification is excellent alternative modeling because it reduces development costs and time by avoiding governing equations and large aerodynamic database. NN based system identification has successfully identified the quadcopter dynamics with good accuracy. This paper gives an overview of the characteristic of the quadcopter and presents a comprehensive survey of the modeling techniques used to determine the flight dynamics of a quadrotor with a particular focus on NN based system identification method. The presented research works have been classified into different categories such as the first principle modeling, system identification and implementation of NN based system identification in quadcopter platform. Finally, the paper highlights challenges that need to be addressed in developing efficient NN based system identification model for unmanned quadcopter system.

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Section: Fig 12-basic Rbf Nn Structurementioning

confidence: 99%

Neural Network-Based System Identification for Quadcopter Dynamic Modeling: A Review

Pairan¹,

Shamsudin²,

Zulkafli³

2020

JAMEA

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“…1 Particularly, the unknown disturbances of parametric uncertainty and external perpetuation are big challenges to robustness of the quadrotor system. 2 Backstepping is popularly applied in the quadrotor control to decouple the complex system but sensitive to disturbances, 3 based on which advanced methods were introduced. 4,5 Choi and Bang 4 incorporated an adaptive command-filtered backstepping control into quadrotor system, with online parameter update laws used to compensate for the uncertain parameters, and command filters to implement the vehicle’s actuator and state constraints.…”

Section: Introductionmentioning

confidence: 99%

Binary observers based control for quadrotor unmanned aerial vehicle with disturbances and measurement delay

Zhang

Zhou

et al. 2017

Proceedings of the Institution of Mechanical Engineers, Part G:

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This paper presents a binary observers based linearization feedback sliding mode controller for quadrotor subjected to disturbances and measurement delay. The quadrotor unmanned aerial vehicle is divided into two subsystems, i.e. rotational subsystem and translational subsystem. Disturbance observer based controller is proposed for the former sensitive to disturbances, and binary observers based one for the latter subjected to both disturbances and measurement delay. Binary observers, consisting of disturbance observer and delayed output observer with their interactions being considered, promise exponential convergences of estimations on disturbance and output to their real values, which are compensated in the feedback control loop. The stabilities of the closed-loop subsystems are demonstrated based on Lyapunov theory. In the presence of both disturbances and measurement delay, simulations are carried out to corroborate the effectiveness and robustness of the proposed controller with superior control inputs.

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“…Furthermore, rapid manoeuvrability, easy maintainability, low-cost manufacturability with reduced mechanical complexity and stable hovering capability above a target, make it a well-suited platform for various tasks, such as small area monitoring, surveillance, damage assessment, mapping after disasters, detection in restricted terrains and reconnaissance, as well as search and rescue missions (Tomic et al, 2012). For such purposes, a considerable number of quadrotors having a variety of structures and properties have been manufactured in various research projects, such as Castillo et al (2005), Hoffman et al (2004), Guenard et al (2008), Bouabdullah (2007) and Meier et al (2011), and some recent types of quadrotors with tilting propellers and new configurations have been constructed by Oner et al (2008), Sanchez et al (2008), Meier et al (2011), Senkul and Altug˘(2013), Driessen and Pounds (2013), Ryll et al (2014) and Wu et al (2014). Some results considering the stability and performance analysis are discussed by Toledo et al (2015); parallel to this study, Kalantari and Spenko (2015) presents modelling and performance assessment of a hybrid terrestrial/aerial quadrotor.…”

Section: Introductionmentioning

confidence: 99%

Feedback control strategies for quadrotor-type aerial robots: a survey

Özbek

Önkol

Efe

2015

Transactions of the Institute of Measurement and Control

113

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Control of aerial robots is a popular research field as applications with different payloads lead to a variety of flight missions. Quadrotor-type unmanned systems are one such example considered in this paper. The performance in any flight experiment depends strictly on the chosen feedback control scheme, which is the core issue addressed in the paper. A number of approaches have been reported in the literature and this paper presents a survey of these schemes with an in-depth discussion of recent research outcomes. A detailed performance evaluation of the controllers, namely proportional-integral-derivative control, sliding mode control, backstepping control, feedback linearization-based control and fuzzy control schemes, are presented. Due to the popularity of the quadrotor-type aerial vehicles, the contribution of the current work is to provide an in-depth guide to the autopilot designers of quadrotor-type unmanned aerial vehicles.

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Modeling and control approach to a distinctive quadrotor helicopter

Cited by 52 publications

References 31 publications

Neural Network-Based System Identification for Quadcopter Dynamic Modeling: A Review

Neural Network-Based System Identification for Quadcopter Dynamic Modeling: A Review

Binary observers based control for quadrotor unmanned aerial vehicle with disturbances and measurement delay

Feedback control strategies for quadrotor-type aerial robots: a survey

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