A Survey and Categorization of Small Low-Cost Unmanned Aerial Vehicle System Identification

Hoffer, Nathan V.; Coopmans, Calvin; Jensen, Austin M.; Chen, YangQuan

doi:10.1007/s10846-013-9931-6

Cited by 99 publications

(43 citation statements)

References 60 publications

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“…Interested readers can refer to other modeling work for small-scale rotorcraft UAVs via two surveys presented in [41,49]. A number of books that provide details on CIFERbased system identification [59,64,136] are also recommended.…”

Section: Dynamics Modeling Of Small-scale Rotorcraft Uavsmentioning

confidence: 99%

“…Other successful results of dynamics modeling for smallscale fixed-wing UAVs are summarized in a comprehensive overview [41]. Interested readers are also referred to a textbook [54] for more detailed background knowledge on system identification and its application to fixed-wing aircraft.…”

Section: Dynamics Modeling Of Small-scale Fixed-wing Uavsmentioning

confidence: 99%

“…For rudimentary level, some introductory documents such as [5,7] can bring readers a basic understanding of UAV's development on various essential issues such as design, operation, sensing, development, automation and autonomy, safety assessment and deployment. From the technical and scientific perspective, the leading-edge research on small-scale UAVs is commonly documented in topic basis, such as autopilot system [15], flapping-wing platform design [33,123], flapping aerodynamics modeling [4], system identification [41], rotorcraft UAV flight control [65], rotorcraft UAV GNC (guidance, navigation, and control) [49] and path planning [60], fixed-wing UAV path following [132], etc. Besides, some strategic documents such as the unmanned systems integrated roadmap series [121] can help readers predict the future of small-scale UAVs in military and defense applications.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Survey of Small-Scale Unmanned Aerial Vehicles: Recent Advances and Future Development Trends

2014

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This paper provides a brief overview on the recent advances of small-scale unmanned aerial vehicles (UAVs) from the perspective of platforms, key elements, and scientific research. The survey starts with an introduction of the recent advances of small-scale UAV platforms, based on the information summarized from 132 models available worldwide. Next, the evolvement of the key elements, including onboard processing units, navigation sensors, mission-oriented sensors, communication modules, and ground control station, is presented and analyzed. Third, achievements of small-scale UAV research, particularly on platform design and construction, dynamics modeling, and flight control, are introduced. Finally, the future of small-scale UAVs' research, civil applications, and military applications are forecasted.

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Section: Dynamics Modeling Of Small-scale Rotorcraft Uavsmentioning

confidence: 99%

Section: Dynamics Modeling Of Small-scale Fixed-wing Uavsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Survey of Small-Scale Unmanned Aerial Vehicles: Recent Advances and Future Development Trends

2014

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“…A requirement is the use of sophisticated system identification methods to obtain the model of the aerial vehicle which is time-consuming task. The detailed steps and several methods for system identification and parameter estimation of aerial vehicles are discussed in [23]. What is more, the working conditions are always changing resulting in a fact that adaptability is a must.…”

Section: Introductionmentioning

confidence: 99%

Learning Control of Fixed-Wing Unmanned Aerial Vehicles Using Fuzzy Neural Networks

Kayacan

Khanesar

Hervás

et al. 2017

International Journal of Aerospace Engineering

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A learning control strategy is preferred for the control and guidance of a fixed-wing unmanned aerial vehicle to deal with lack of modeling and flight uncertainties. For learning the plant model as well as changing working conditions online, a fuzzy neural network (FNN) is used in parallel with a conventional P (proportional) controller. Among the learning algorithms in the literature, a derivative-free one, sliding mode control (SMC) theory-based learning algorithm, is preferred as it has been proved to be computationally efficient in real-time applications. Its proven robustness and finite time converging nature make the learning algorithm appropriate for controlling an unmanned aerial vehicle as the computational power is always limited in unmanned aerial vehicles (UAVs). The parameter update rules and stability conditions of the learning are derived, and the proof of the stability of the learning algorithm is shown by using a candidate Lyapunov function. Intensive simulations are performed to illustrate the applicability of the proposed controller which includes the tracking of a three-dimensional trajectory by the UAV subject to timevarying wind conditions. The simulation results show the efficiency of the proposed control algorithm, especially in real-time control systems because of its computational efficiency.

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“…Aerial Vehicles (AVs) have been used more and more widely in surveillance, search and rescue mission, aerial mapping, cartography, border patrol, inspection, agricultural imaging, and other related areas [1][2][3]. During these flight missions, the AVs usually fly in low altitudes, which makes them be subject to collision with static obstacles (such as buildings, trees, mountains, etc.)…”

Section: Introductionmentioning

confidence: 99%

Spatial Trajectory Tracking Control of a Fully Actuated Helicopter in Known Static Environment

Zhao

et al. 2016

J Intell Robot Syst

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In this paper, we consider the control problem of tracking a 3D spatial trajectory for a fully actuated helicopter in static known environment, which is predefined to avoid obstacles and collisions considering the distance, fuel consumption and other related constraints. For this purpose, a nonlinear controller using the radial basis function neural network (RBFNN) is designed. Based on Lyapunov analysis, the proposed adaptive neural network control succeeds in tracking the desired trajectory robustly to a small neighborhood of zero, and guarantees the boundedness of all the closed-loop signals at the same time.Extensive numerical results are given to illustrate the effectiveness of the designed controller.

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A Survey and Categorization of Small Low-Cost Unmanned Aerial Vehicle System Identification

Cited by 99 publications

References 60 publications

A Survey of Small-Scale Unmanned Aerial Vehicles: Recent Advances and Future Development Trends

A Survey of Small-Scale Unmanned Aerial Vehicles: Recent Advances and Future Development Trends

Learning Control of Fixed-Wing Unmanned Aerial Vehicles Using Fuzzy Neural Networks

Spatial Trajectory Tracking Control of a Fully Actuated Helicopter in Known Static Environment

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