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

2021 ◽  
Vol 11 (4) ◽  
pp. 1546
Author(s):  
Qudrat Khan ◽  
Rini Akmeliawati
Keyword(s):  
Mathematical Modeling ◽  
System Identification ◽  
Domain Knowledge ◽  
Micro Air Vehicles ◽  
Flapping Wing ◽  
Mean Square ◽  
Least Mean Square ◽  
Aerial Vehicles ◽  
Recent Developments ◽  
Identification Techniques

This paper presents a thorough review on the system identification techniques applied to flapping wing micro air vehicles (FWMAVs). The main advantage of this work is to provide a solid background and domain knowledge of system identification for further investigations in the field of FWMAVs. In the system identification context, the flapping wing systems are first categorized into tailed and tailless MAVs. The most recent developments related to such systems are reported. The system identification techniques used for FWMAVs can be classified into time-response based identification, frequency-response based identification, and the computational fluid-dynamics based computation. In the system identification scenario, least mean square estimation is used for a beetle mimicking system recognition. In the end, this review work is concluded and some recommendations for the researchers working in this area are presented.

An Improved ZA-LMS Algorithm for Sparse System Identification with Variable Sparsity

2014 ◽  
Vol 602-605 ◽  
pp. 2415-2419 ◽  
Author(s):  
Hui Luo ◽  
Yun Lin ◽  
Qing Xia
Keyword(s):  
System Identification ◽  
Impulse Response ◽  
Lms Algorithm ◽  
Mean Square ◽  
Least Mean Square ◽  
Sparse System ◽  
Sparse System Identification ◽  
Least Mean Square Algorithm ◽  
Improved Algorithm

The standard least mean square algorithm does not consider the sparsity of the impulse response,and the performs of the ZA-LMS algorithm deteriorates ,as the degree of system sparsity reduces or non-sparse . Concerning this issue ,the ZA-LMS algorithm is studied and modified in this paper to improve the performance of sparse system identification .The improved algorithm by modify the zero attraction term, which attracts the coefficients only in a certain range (the “inactive” taps), thus have a good performance when the sparsity decreases. The simulations demonstrate that the proposed algorithm significantly outperforms then the ZA-LMS with variable sparisity.

scholarly journals A Review of Propulsion, Power, and Control Architectures for Insect-Scale Flapping-Wing Vehicles

2018 ◽  
Vol 70 (1) ◽  
Author(s):  
E. Farrell Helbling ◽  
Robert J. Wood
Keyword(s):  
Integrated Circuit ◽  
Micro Air Vehicles ◽  
High Energy ◽  
Flapping Wing ◽  
High Energy Density ◽  
Small Scale ◽  
Flying Insects ◽  
Power And Control ◽  
Recent Developments ◽  
And Control

Flying insects are able to navigate complex and highly dynamic environments, can rapidly change their flight speeds and directions, are robust to environmental disturbances, and are capable of long migratory flights. However, flying robots at similar scales have not yet demonstrated these characteristics autonomously. Recent advances in mesoscale manufacturing, novel actuation, control, and custom integrated circuit (IC) design have enabled the design of insect-scale flapping wing micro air vehicles (MAVs). However, there remain numerous constraints to component technologies—for example, scalable high-energy density power storage—that limit their functionality. This paper highlights the recent developments in the design of small-scale flapping wing MAVs, specifically discussing the various power and actuation technologies selected at various vehicle scales as well as the control architecture and avionics onboard the vehicle. We also outline the challenges associated with creating an integrated insect-scale flapping wing MAV.

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