This paper describes the conceptual design of a micro coaxial unmanned aerial vehicle (MCR UAV v3.0) based on its flight dynamics and a simple aerodynamic analysis using computational fluid dynamics (CFD). In addition, a simple linear control is proposed with the pole assignment technique. The methodology proposed in this paper involves a standardized path for designing the novel micro coaxial UAV. This begins by selecting the avionics to create a primary dimensional design for a later transient and stationary CFD analysis. In effect, the mathematical model is obtained using the Newton–Euler formulation and is linearized to obtain the dynamical requirements of the vehicle. The requirements allow us to design the control scheme with a linear control technique. This process is iterative and uses a combination of flight dynamics and CFD. The control technique is based on pole assignment, ensuring a specific phase condition is used in the controller gain for the stabilization of the proposed aerial vehicle. The control scheme is analyzed once the CFD analysis is correctly performed; in this sense, the methodology proposed in this paper is capable of converging as a result of the dimensional design. This design ensures a suitable vehicle performance according to the dynamical requirements. Thus, the micro coaxial UAV is completely designed based on its flight dynamics along with a CFD analysis, generating a robust methodology.

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SUMMARYIn this paper, a robust geometric navigation algorithm, designed on the special Euclidean group SE(3), of a quadrotor is proposed. The equations of motion for the quadrotor are obtained using the Newton–Euler formulation. The geometric navigation considers a guidance frame which is designed to perform autonomous flights with a convergence to the contour of the task with small normal velocity. For this purpose, a super twisting algorithm controls the nonlinear rotational and translational dynamics as a cascade structure in order to establish the fast and yet smooth tracking with the typical robustness of sliding modes. In this sense, the controller provides robustness against parameter uncertainty, disturbances, convergence to the sliding manifold in finite time, and asymptotic convergence of the trajectory tracking. The algorithm validation is presented through experimental results showing the feasibility of the proposed approach and illustrating that the tracking errors converge asymptotically to the origin.

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