Review on System Identification and Mathematical Modeling of Flapping Wing Micro-Aerial Vehicles

Khan, Qudrat; Akmeliawati, Rini

doi:10.3390/app11041546

Cited by 12 publications

(5 citation statements)

References 144 publications

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“…Shyy et al [35] Flapping and flexible wings in MAVs Sane [44] Aerodynamic modeling Knowles et al [36] Evolution of insect-inspired MAVs Orlowski and Girard [47] Dynamics, stability and control of FWMAVs Chin and Lentink [45] Aerodynamics of flapping wing Phan and Park [39] Tail-less insect-inspired FWMAVs Chen and Zhang [42] Design and fabrication Haider et al [43] FWMAVs progress and application Khan and Akmeliawati [49] System identification & mathematical modeling Xiao et al [41] Hover capable FWMAVs Meng et al [50] Perching and grasping MAVs Abdul Hamid and Filippone [46] Aerodynamic modeling for flapping insects Wang et al [38] Ultra lightweight flapping wing vehicles…”

Section: Author Key Aspectsmentioning

confidence: 99%

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Landing and take-off capabilities of bioinspired aerial vehicles: a review

Hammad,

Armanini

2024

Bioinspir. Biomim.

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Bioinspired Flapping-Wing Micro Aerial Vehicles (FWMAVs) haveemerged over the last two decades as a promising new type of robot. Their high thrustto-weight ratio, versatility, safety, and maneuverability, especially at small scales,could make them more suitable than fixed-wing and multi-rotor vehicles for variousapplications, especially in cluttered, confined environments and in close proximity tohumans, flora, and fauna. Unlike natural flyers, however, most FWMAVs currentlyhave limited take-off and landing capabilities. Natural flyers are able to take offand land effortlessly from a wide variety of surfaces and in complex environments.Mimicking such capabilities on flapping-wing robots would considerably enhance theirpractical usage. This review presents an overview of take-off and landing techniques forFWMAVs, covering different approaches and mechanism designs, as well as dynamicsand control aspects. The special case of perching is also included. As well as discussingsolutions investigated for FWMAVs specifically, we also present solutions that havebeen developed for different types of robots but may be applicable to flapping-wingones. Different approaches are compared and their suitability for different applicationsand types of robots is assessed. Moreover, research and technology gaps are identified,and promising future work directions are identified.

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Section: Author Key Aspectsmentioning

confidence: 99%

“…Reviews on the flapping wing micro aerial vehicle's dynamics, stability, and control aspects have been conducted by Orlowski and Girard [47] and Taha et al [48]. Khan and Akmeliawati [49] conducted a review of system identification techniques and mathematical modeling. A summary of the previous reviews is given in table 2.…”

Section: Previous Reviews On Fwmavsmentioning

confidence: 99%

Landing and take-off capabilities of bioinspired aerial vehicles: a review

Hammad,

Armanini

2024

Bioinspir. Biomim.

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“…Moreover, model calibration is closely related to monitoring algorithms, which are reviewed in [21], focusing on rolling stock health monitoring and fault detection. Regarding other fields of transport, motion model calibration is also covered in the case of wheeled vehicles [22], flapping wing micro-aerial vehicles [23], and aircrafts [24].…”

Section: Introductionmentioning

confidence: 99%

A Literature Review on Train Motion Model Calibration

Cunillera

Bešinović

Lentink

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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The dynamics of a moving train are usually described by means of a motion model based on Newton's second law. This model uses as input track geometry data and train characteristics like mass, the parameters that model the running resistance, the maximum tractive effort and power, and the brake rates to be applied. It can reproduce and predict train dynamics accurately if the mentioned train characteristics are carefully calibrated. The model constitutes the core element of a broad variety of railway applications, from timetabling tools to Driver Advisory Systems and Automatic Train Operation. Among the existing train motion model calibration techniques, those that use operational data are of particular interest, as they benefit from on-board recorded data, capturing the train dynamics during operation. In this literature review article we provide an overview of the train motion model calibration techniques that have been published in the scientific literature between January 2000 and December 2021 and either use operational data or can be minimally adapted to use it. To this end, we present a critical overview of the existing train motion model calibration approaches, distinguishing online calibration that analyzes data on-the-go and offline calibration that analyzes historical data batchwise. We propose a research agenda and highlight some potential goals to be tackled in the near future: from devising accurate online calibrators for eco-driving applications to quantitizing the physical sources of parameter variation. Last, we discuss practical recommendations for practitioners and scholars inferred from the current state of the art.

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“…Many efforts have been made to model the flapping kinematics, aerodynamics, and flight dynamics of flyers and FWMAVs. They are derived computationally or experimentally with various assumptions, ranging from time-invariant linear to nonlinear time-periodic models [58][59][60]. One of the main purposes of obtaining a dynamic model is to design a flight controller with various control techniques and schemes, such as PID [34,61], linear-quadratic-Gaussian [62], state feedback [63,64], nonlinear [65][66][67], sliding mode [68,69], robust [70,71], adaptive [72], adaptive neural network [73,74], and model-free [75].…”

Section: Introductionmentioning

confidence: 99%

Longitudinal Mode System Identification of an Insect-like Tailless Flapping-Wing Micro Air Vehicle Using Onboard Sensors

Aurecianus

Park

et al. 2022

Applied Sciences

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In this paper, model parameter identification results are presented for a longitudinal mode dynamic model of an insect-like tailless flapping-wing micro air vehicle (FWMAV) using angle and angular rate data from onboard sensors only. A gray box model approach with indirect method was utilized with adaptive Gauss–Newton, Levenberg–Marquardt, and gradient search identification methods. Regular and low-frequency reference commands were mainly used for identification since they gave higher fit percentages than irregular and high-frequency reference commands. Dynamic parameters obtained using three identification methods with two different datasets were similar to each other, indicating that the obtained dynamic model was sufficiently reliable. Most of the identified dynamic model parameters had similar values to the computationally obtained ones, except stability derivatives for pitching moment with forward velocity and pitching rate variations. Differences were mainly due to certain neglected body, nonlinear dynamics, and the shift of the center of gravity. Fit percentage of the identified dynamic model (~49%) was more than two-fold higher than that of the computationally obtained one (~22%). Frequency domain analysis showed that the identified model was much different from that of the computationally obtained one in the frequency range of 0.3 rad/s to 5 rad/s, which affected transient responses. Both dynamic models showed that the phase margin was very low, and that it should be increased by a feedback controller to have a robustly stable system. The stable dominant pole of the identified model had a higher magnitude which resulted in faster responses. The identified dynamic model exhibited much closer responses to experimental flight data in pitching motion than the computationally obtained dynamic model, demonstrating that the identified dynamic model could be used for the design of more effective pitch angle-stabilizing controllers.

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Review on System Identification and Mathematical Modeling of Flapping Wing Micro-Aerial Vehicles

Cited by 12 publications

References 144 publications

Landing and take-off capabilities of bioinspired aerial vehicles: a review

Landing and take-off capabilities of bioinspired aerial vehicles: a review

A Literature Review on Train Motion Model Calibration

Longitudinal Mode System Identification of an Insect-like Tailless Flapping-Wing Micro Air Vehicle Using Onboard Sensors

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