A survey on quadrotors: Configurations, modeling and identification, control, collision avoidance, fault diagnosis and tolerant control

Shraim, Hassan; Awada, Ali; Youness, Rafic

doi:10.1109/maes.2018.160246

Cited by 138 publications

(61 citation statements)

References 152 publications

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“…Computation and control Autopilot platforms No Yes Cell N/A [27], 2018 [28], 2015 [29], 2012 CPS vision into the UAV networks is promising to significantly improve the performance of the whole system.…”

Section: A Cps Can Inspire the Uav Networkmentioning

confidence: 99%

“…[24]- [26] have surveyed the popular computation platforms for the UAVs. The control-related matters, including the control algorithms for single UAV and the formation control and coordination for multiple cooperative UAVs, are respectively investigated in [27]- [29] and [30], [31]. From the physical issue perspective, mobility models are presented for single UAV in [32] and for collective UAVs in [33].…”

Section: B Existing Surveys and Motivationsmentioning

confidence: 99%

See 1 more Smart Citation

Survey on Unmanned Aerial Vehicle Networks: A Cyber Physical System Perspective

Wang

Zhao

Zhang

et al. 2020

IEEE Commun. Surv. Tutorials

170

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Unmanned aerial vehicle (UAV) networks are playing an important role in various areas due to their agility and versatility, which have attracted significant attention from both the academia and industry in recent years. As an integration of the embedded systems with communication devices, computation capabilities and control modules, the UAV network could build a closed loop from data perceiving, information exchanging, decision making to the final execution, which tightly integrates the cyber processes into the physical devices. Therefore, the UAV network could be considered as a cyber physical system (CPS). Revealing the coupling effects among the three interacted components in this CPS system, i.e., communication, computation and control, is envisioned as the key to properly utilize all the available resources and hence improve the performance of the UAV networks. In this paper, we present a comprehensive survey on the UAV networks from a CPS perspective. Firstly, we respectively research the basics and advances with respect to the three CPS components in the UAV networks. Then we look inside to investigate how these components contribute to the system performance by classifying the UAV networks into three hierarchies, i.e., the cell level, the system level, and the system of system level. Further, the coupling effects among these CPS components are explicitly illustrated, which could be enlightening to deal with the challenges in each individual aspect. New research directions and open issues are discussed at the end of this survey. With this intensive literature review, we try to provide a novel insight into the state-of-the-art in the UAV networks.

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Section: A Cps Can Inspire the Uav Networkmentioning

confidence: 99%

Section: B Existing Surveys and Motivationsmentioning

confidence: 99%

Survey on Unmanned Aerial Vehicle Networks: A Cyber Physical System Perspective

Wang

Zhao

Zhang

et al. 2020

IEEE Commun. Surv. Tutorials

170

View full text Add to dashboard Cite

show abstract

“…[3][4][5][6][7] Indeed, it can be used as a powerful alternative to the approaches that try to obtain the dynamic model of a system using basic physical laws suffering from several complexities and inaccuracies for complex systems. 8 System identification can be performed in both frequency and time domains. 9 Frequency domain-based identification generally leads to the linear estimation of the dynamic model of the system, while the time-domain based approaches can be used to obtain both linear and nonlinear dynamic models of the system.…”

Section: Introductionmentioning

confidence: 99%

“…There are several studies in the literature which have addressed the significant advantages of system identification in comparison with other conventional methods of determining the system dynamics, especially for complex nonlinear systems 3‐7 . Indeed, it can be used as a powerful alternative to the approaches that try to obtain the dynamic model of a system using basic physical laws suffering from several complexities and inaccuracies for complex systems 8 . System identification can be performed in both frequency and time domains 9 .…”

Section: Introductionmentioning

confidence: 99%

Identification of nonlinear time‐varying systems using wavelet neural networks

Emami

Roudbari

2020

Adv Control Appl

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The dynamic model of an aircraft changes significantly by altering the flight speed and the vehicle altitude. Thus, a conventional aircraft has a nonlinear time‐varying dynamic model in different regions of the flight envelope, and a dynamic model developed for a specific operating point is not valid in the entire flight envelope. This paper presents a novel identification approach that can deal with nonlinear and time‐varying characteristics of complex dynamic systems, especially an aerial vehicle in the entire flight envelope. In this regard, a set of local submodels are first developed at different operating points of the system, and subsequently, a multimodel structure is introduced to aggregate the outputs of the local models as a single model. Wavelet Neural Networks (WNNs), which combine both the universal approximation property of neural networks and the wavelet decomposition capability, are used as the local models of the proposed scheme. Also, three different approaches for determining the validity functions of the local models are introduced to allow for identifying the time‐varying dynamics of the system. The simulation results obtained for the Generic Transport Model aircraft suggest that the proposed WNN‐based multimodel structure can be used satisfactorily as the prediction model of model‐based flight control systems for long prediction horizons.

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“…Applications for a UAV include road traffic management, mapping, tracking, search and rescue, exploration, inspector of borders and impassable areas, etc. [1,2]. The application of UAVs is very important where a human presence is hazardous.…”

Section: Introductionmentioning

confidence: 99%

Design of an Intelligent Controller for Station Keeping, Attitude Control, and Path Tracking of a Quadrotor Using Recursive Neural Networks

Khosravian

Maghsoudi

2019

IJE

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A B S T R A C TDuring recent years there has been growing interest in unmanned aerial vehicles (UAVs). Moreover, the necessity to control and navigate these vehicles has attracted much attention from researchers in this field. T his is mostly due to the fact that the interactions between turbulent airflows apply complex aerodynamic forces to the system. Since the dynamics of a quadrotor are non-linear and the system is a multivariable one, moreover, it has six degrees of freedom for only four control inputs, then it is an under actuated system. T his is why conventional control algorithms employed to track desired trajectories of fully actuated aerial vehicles are no longer applicable for quadrotors. The main step in the manufacturing of a fully autonomous unmanned aerial vehicle is to design a controller which stabilizes the aerial vehicle in the presence of uncertainties and disturbances, then navigate it along a desired trajectory. T he aim of this study is to design and implement an intelligent controller for station keeping, attitude control, and path tracking of a quadrotor. For this purpose, an artificial neural network method was employed. The artificial neural network is one of the most powerful and useful tools in the modification of a control system. In this study, the control methods conventionally applied to quadrotors are reviewed at first. T hen, in order to analyze the behavior of the system and also to design the controller, the state equations of a quadrotor are discussed. Following that , the design of a recurrent neural network based non-linear PID control algorithm is presented. Finally, the results of the simulation performed are presented, and the performance of the proposed algorithm are investigated. It was shown that by using the proposed algorithm, the quadrotor tracks the desired trajectory, and simultaneously, its attitude is stabilized.

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A survey on quadrotors: Configurations, modeling and identification, control, collision avoidance, fault diagnosis and tolerant control

Cited by 138 publications

References 152 publications

Survey on Unmanned Aerial Vehicle Networks: A Cyber Physical System Perspective

Survey on Unmanned Aerial Vehicle Networks: A Cyber Physical System Perspective

Identification of nonlinear time‐varying systems using wavelet neural networks

Design of an Intelligent Controller for Station Keeping, Attitude Control, and Path Tracking of a Quadrotor Using Recursive Neural Networks

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