A flight-dynamics oriented simulation model of a flapping-wing Micro Air Vehicle (MAV) has been developed. This concept is based on flapping flight performed in nature by insects or hummingbirds. An optimization of the flapping kinematics of the wing has been led, in order to maximize the mean lift and thus the payload. A neural network has been designed to reproduce the function shape of the wings movements, and the weights have been optimized using a genetic algorithm. Results show a lift gain from 30 to 40%, and corroborate also some mechanisms shown up through experiments.
Abstract-Vector control has been widely used in control of permanent magnet synchronous motors (PMSM). This technique requires an accurate knowledge of the rotor position. In many applications, such as mini-UAV, it is not possible to install any mechanical sensor. Therefore, a sensorless technique using position observer has to be implemented in order to estimate the rotor position. In this paper, we propose a new observer for PMSM sensorless control: the position estimate is generated upon electromagnetic relations (dynamics of magnetic flux in the motor) in the two-phase stationary reference frame. The implementation of the proposed observer is not a real burden; its validity is experimentally tested and satisfactory results are obtained regarding real position (and speed set-point) tracking in different operating situations.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.