Identification of Flight Vehicle Models Using Fuzzified Eigensystem Realization Algorithm

Lin, Chun-Liang; Huang, Chun-Hsi; Lee, Chia-Sung; Chao, Maw-Jyi

doi:10.1109/tie.2011.2116760

Cited by 6 publications

(3 citation statements)

References 29 publications

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“…The identification method requires a fair amount of flight data, which in turn requires a pre-processing stage that includes human expertise. Similar drawbacks are present in the approach used by [17] where UAV models based on fuzzified eigensystem realization algorithm were identified.…”

Section: A Relation To Existing Adaptive Control Approachesmentioning

confidence: 76%

See 1 more Smart Citation

Multirotors from Takeoff to Real-Time Full Identification Using the Modified Relay Feedback Test and Deep Neural Networks

Ayyad¹,

Chehadeh²,

Wahbah³

et al. 2020

Preprint

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Low cost real-time identification of multirotor unmanned aerial vehicle (UAV) dynamics is an active area of research supported by the surge in demand and emerging application domains. Such real-time identification capabilities shorten development time and cost, making UAVs' technology more accessible, and enable a variety of advanced applications. In this paper, we present a novel comprehensive approach, called DNN-MRFT, for real-time identification and tuning of multirotor UAVs using the Modified Relay Feedback Test (MRFT) and Deep Neural Networks (DNN). The first contribution is the development of a generalized framework for the application of DNN-MRFT to higher-order systems. The second contribution is a method for the exact estimation of identified process gain which mitigates the inaccuracies introduced due to the use of the describing function method in approximating the response of Lure's systems. The third contribution is a generalized controller based on DNN-MRFT that takes-off a UAV with unknown dynamics and identifies the inner loops dynamics in-flight. Using the developed generalized framework, DNN-MRFT is sequentially applied to the outer translational loops of the UAV utilizing in-flight results obtained for the inner attitude loops. DNN-MRFT takes on average 15 seconds to get the full knowledge of multirotor UAV dynamics and was tested on multiple designs and sizes. The identification accuracy of DNN-MRFT is demonstrated by the ability of a UAV to pass through a vertical window without any further tuning, calibration, or feedforward terms. Such demonstrated accuracy, speed, and robustness of identification pushes the limits of state-of-the-art in real-time identification of UAVs.

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Section: A Relation To Existing Adaptive Control Approachesmentioning

confidence: 76%

“…The optimization decision variables are the parameters K p , K i , δ p , and h (MRFT amplitude) present in Eq. (17). A cost function has been designed to address the optimization of these values:…”

Section: Design Of Take-off Controllermentioning

confidence: 99%

Multirotors from Takeoff to Real-Time Full Identification Using the Modified Relay Feedback Test and Deep Neural Networks

Ayyad¹,

Chehadeh²,

Wahbah³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…and ψ i are the UAV agent's roll, pitch, and yaw angles, respectively [14], [27]. The linear and angular velocities of A i in the body fixed frame are denoted by…”

Section: B Low-level Control Design For Generic Fixed-wing Uavsmentioning

confidence: 99%

Distributed Cohesive Motion Control of Flight Vehicle Formations

Bayezit

Fi̇dan

2013

IEEE Trans. Ind. Electron.

181

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In this paper, we consider the problem of decentralized cohesive motion control of a formation of autonomous vehicles or robots moving in three dimensions, where the formation is required to move from its initial setting (defined by the positions of the agents in the formation) to a final desired setting and, during this motion, maintain its formation geometry defined by the initial distances between the agent pairs. We propose a distributed control scheme to solve this problem utilizing the notions of graph rigidity and persistence as well as techniques of virtual target tracking and smooth switching. The distributed control scheme is developed by modeling the agent kinematics as single-velocity integrator; nevertheless, extension to the cases with practical kinematic and dynamic models of fixed-wing autonomous aerial vehicles and quadrotors is discussed. In this context, we examine the maintenance of geometric formation of a swarm of autonomous flight vehicles. The developed coordination and control schemes are verified via a number of simulations.

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Discrete-time realization of transcendental impedance models, with application to modeling spherical solid diffusion

Lee

Chemistruck

Plett

2012

Journal of Power Sources

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Identification of Flight Vehicle Models Using Fuzzified Eigensystem Realization Algorithm

Cited by 6 publications

References 29 publications

Multirotors from Takeoff to Real-Time Full Identification Using the Modified Relay Feedback Test and Deep Neural Networks

Multirotors from Takeoff to Real-Time Full Identification Using the Modified Relay Feedback Test and Deep Neural Networks

Distributed Cohesive Motion Control of Flight Vehicle Formations

Discrete-time realization of transcendental impedance models, with application to modeling spherical solid diffusion

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