journal article·2019-01-31

Modeling and control of flapping wing micro aerial vehicles

Shiba Biswal, Marc Mignolet, Armando A Rodriguez

Bioinspiration & Biomimetics· Volume 14· Issue 2
Publisher page

Abstract

Research in robots that emulate insect flight or micro aerial vehicles (MAV) has gained significant momentum in the past decade owing to the vast number of fields they could be employed in. In this paper, key modeling and control aspects of a flapping wing MAV in hover have been discussed. Models of varying complexity have been developed by previous researchers. Here, we examine the validity of key assumptions involved in some of these models in a closed-loop control setting. Every model has limitations and with proper design of feedback control these limitations can be overcome up to a certain degree. Three nonlinear models with increasing complexity have been developed. Model I includes only the rigid body dynamics while ignoring the wing dynamics while model II includes the rigid body dynamics along with the wing kinematics. Lastly, model III encompasses the complete rigid body and the rigid wing dynamics. To ensure these higher fidelity models can be rendered unnecessary with a suitably designed controller, a method is presented wherein the controller is designed for the simplest model and tested for its robustness on the more complex models. Linear quadratic regulator (LQR) is used as the main control system design methodology. A nonlinear parameter optimization algorithm is employed to design a family of LQR control systems for the MAV. Additionally, critical performance trade-offs are illuminated, and properties at both the plant output and input are examined. Lastly, we also provide specific rules of thumb for the control system design.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Biomimetic flight and propulsion mechanisms · Distributed Control Multi-Agent Systems · Micro and Nano Robotics

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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