scholarly journals Dynamic Stability and Flight Control of Biomimetic Flapping-Wing Micro Air Vehicle

Aerospace ◽  
2021 ◽  
Vol 8 (12) ◽  
pp. 362
Author(s):  
Muhammad Yousaf Bhatti ◽  
Sang-Gil Lee ◽  
Jae-Hung Han

This paper proposes an approach to analyze the dynamic stability and develop trajectory-tracking controllers for flapping-wing micro air vehicle (FWMAV). A multibody dynamics simulation framework coupled with a modified quasi-steady aerodynamic model was implemented for stability analysis, which was appended with flight control block for accomplishing various flight objectives. A gradient-based trim search algorithm was employed to obtain the trim conditions by solving the fully coupled nonlinear equations of motion at various flight speeds. Eigenmode analysis showed instability that grew with the flight speed in longitudinal dynamics. Using the trim conditions, we linearized dynamic equations of FWMAV to obtain the optimal gain matrices for various flight speeds using the linear-quadratic regulator (LQR) technique. The gain matrices from each of the linearized equations were used for gain scheduling with respect to forward flight speed. The reference tracking augmented LQR control was implemented to achieve transition flight tracking that involves hovering, acceleration, and deceleration phases. The control parameters were updated once in a wingbeat cycle and were changed smoothly to avoid any discontinuities during simulations. Moreover, trajectories tracking control was achieved successfully using a dual loop control approach. Control simulations showed that the proposed controllers worked effectively for this fairly nonlinear multibody system.

Author(s):  
Monica Sam ◽  
Sanjay Boddhu ◽  
Kayleigh Duncan ◽  
Hermanus Botha ◽  
John Gallagher

Much effort has gone into improving the performance of evolutionary algorithms that augment traditional control in a Flapping Wing Micro Air Vehicle. An EA applied to such a vehicle in flight is expected to evolve solutions quickly to prevent disruptions in following the desired flight trajectory. Time to evolve solutions therefore is a major criterion by which performance of an algorithm is evaluated. This paper presents results of applying an assortment of different evolutionary algorithms to the problem. This paper also presents some discussion on which choices for representation and algorithm parameters would be optimal for the flight control problem and the rationale behind it. The authors also present a guided sampling approach of the search space to make use of the redundancy of workable solutions found in the search space. This approach has been demonstrated to improve learning times when applied to the problem.


2016 ◽  
Vol 13 (3) ◽  
pp. 458-467 ◽  
Author(s):  
Sriyulianti Widhiarini ◽  
Ji Hwan Park ◽  
Bum Soo Yoon ◽  
Kwang Joon Yoon ◽  
Il-Hyun Paik ◽  
...  

ROBOT ◽  
2011 ◽  
Vol 33 (3) ◽  
pp. 366-370 ◽  
Author(s):  
Pengcheng CHI ◽  
Weiping ZHANG ◽  
Wenyuan CHEN ◽  
Hongyi LI ◽  
Kun MENG ◽  
...  

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