Neural dynamics for adaptive attitude tracking control of a flapping wing micro aerial vehicle

Liu, Mei; Ma, Dexiu; Ли, Шуай

doi:10.1016/j.neucom.2021.05.088

Cited by 8 publications

(3 citation statements)

References 44 publications

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“…PID Control [57,65] Simple and easy to implement, fast response time, and a wide range of applications Dependent on precise adjustment of parameters, sensitive to system modeling errors, and not adaptive Adaptive Control [18,66] Self-adjustable control parameters according to the real-time status of the system Adaptive factors are process independent and require additional conditions Neural Network Control [67] Strong nonlinear approximation capability is applicable to most nonlinear control problems and can effectively deal with systems with incorrect mathematical descriptions A large amount of training data is required, which is computationally intensive and requires high hardware requirements, and fewer samples will lead to poor system performance Table 1. Cont.…”

Section: Control Methods Advantage Disadvantagementioning

confidence: 99%

Review of the Flight Control Method of a Bird-like Flapping-Wing Air Vehicle

Fang,

Wen,

Gao

et al. 2023

Micromachines

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The Bird-like Flapping-wing Air Vehicle (BFAV) is a robotic innovation that emulates the flight patterns of birds. In comparison to fixed-wing and rotary-wing air vehicles, the BFAV offers superior attributes such as stealth, enhanced maneuverability, strong adaptability, and low noise, which render the BFAV a promising prospect for numerous applications. Consequently, it represents a crucial direction of research in the field of air vehicles for the foreseeable future. However, the flapping-wing vehicle is a nonlinear and unsteady system, posing significant challenges for BFAV to achieve autonomous flying since it is difficult to analyze and characterize using traditional methods and aerodynamics. Hence, flight control as a major key for flapping-wing air vehicles to achieve autonomous flight garners considerable attention from scholars. This paper presents an exposition of the flight principles of BFAV, followed by a comprehensive analysis of various significant factors that impact bird flight. Subsequently, a review of the existing literature on flight control in BFAV is conducted, and the flight control of BFAV is categorized into three distinct components: position control, trajectory tracking control, and formation control. Additionally, the latest advancements in control algorithms for each component are deliberated and analyzed. Ultimately, a projection on forthcoming directions of research is presented.

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Section: Control Methods Advantage Disadvantagementioning

confidence: 99%

Review of the Flight Control Method of a Bird-like Flapping-Wing Air Vehicle

Fang,

Wen,

Gao

et al. 2023

Micromachines

View full text Add to dashboard Cite

show abstract

“…However, to the best of our knowledge, existing works for solving the time-varying WSN localization problem are considerably rare. In addition, as a special type of neurodynamics, zeroing neurodynamics (ZND) is generalized from ZNN and can guarantee that each element of the error function converges to zero [34], [35]. For this reason, a quite implementable and feasible ZND-based method for solving the range-based and time-varying WSN localization problem is provided.…”

Section: Related Workmentioning

confidence: 99%

Modified Zeroing Neurodynamics Models for Range-Based WSN Localization From AOA and TDOA Measurements

Wang

Liu

et al. 2022

IEEE Sensors J.

Self Cite

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With the rise of the internet of things, wireless sensor network (WSN) technology has gained unprecedented development and has attracted increasing attention from researchers. Due to the inherent characteristics of WSN, such as interaction with the environment, WSN localization becomes an essential and attractive topic in academia and industry. In this paper, the range-based localization problem in a mobile WSN application scenario is considered to be time-varying and modeled as a dynamic matrix equation by introducing the time parameter. Two modified zeroing neurodynamics (ZND) models are proposed and investigated to deal with range-based WSN localization problems from angle of arrival (AOA) measurement and time difference of arrival (TDOA) measurement. In addition, the convergence of the proposed models is theoretically analyzed. Furthermore, computer simulations on WSN localization are carried out to prove the effectiveness of the proposed models in terms of accuracy and robustness to the dynamic environment. Additionally, the application to underwater sensor node localization of underwater acoustic network (UAN) testbed is provided to illustrate the feasibilities of the proposed models for solving UAN localization problem.

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“…A micro air vehicle (MAV) is a mechanical device with the ability to fly and can be divided into three types according to the way it generates lift: a fixed-wing flying robot like a jet-propelled aircraft, a rotor-wing vehicle like a helicopter, and a flappingwing robot that mimics birds or insects [1,2]. Micro flappingwing air vehicles have many advantages over the first two types of flight, including their ultra-small size, high flexibility, better manoeuvrability, and excellent hovering ability [3].…”

Section: Introductionmentioning

confidence: 99%

Mechanism Design and Motion Analysis of a Flapping-Wing Air Vehicle

Shi

Guo

et al. 2022

Mathematical Problems in Engineering

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Micro flapping-wing air vehicles (FWAV) are highly appreciated by scientists and scholars around the world due to their small size, high flexibility, and better manoeuvrability. There are huge potential applications of flapping-wing robots in rescue, survey, and search, as they can enter more dangerous and narrow spaces to accomplish tasks that were previously impossible for people. In this paper, we first analyze the flapping motion of birds by reviewing a large amount of information and literature and identifying the sources of lift and thrust during flight. Then, the double crank and double rocker mechanisms are optimized as the flapping mechanism. Proper flapping-wing size is further determined by analyzing the relative position between the driving mechanism and the flapping mechanism. Moreover, a pitch mechanism consisting of ball hinges is designed to realize the wing’s pitching motion for additional thrust. The fuselage and landing gear of the flapping-wing robot are designed for low air resistance and lightweight, and the tail mechanism are designed to have both roll and pitch freedoms, offering excellent manoeuvrability during flight. Simulation results show positive lift force is generated based on the novel pitching design for the small wing. The research in this paper is an important resource for novice researchers to design and study the advanced and complex problems of flapping-wing air vehicles.

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Neural dynamics for adaptive attitude tracking control of a flapping wing micro aerial vehicle

Cited by 8 publications

References 44 publications

Review of the Flight Control Method of a Bird-like Flapping-Wing Air Vehicle

Review of the Flight Control Method of a Bird-like Flapping-Wing Air Vehicle

Modified Zeroing Neurodynamics Models for Range-Based WSN Localization From AOA and TDOA Measurements

Mechanism Design and Motion Analysis of a Flapping-Wing Air Vehicle

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