air vehicle's endurance (sec.) i incidence angle (radians) I x mass moment of inertia about x-axis (Kg.m 2 ) L lift force (N) m number of wing stations solved M x moment about x-axis (N.m) n vortex location q dynamic pressure (Pa) Q amount of electric charge stored in battery R air vehicle's range (m) ABSTRACTThe aerodynamic design optimisation of a Micro Air Vehicle (MAV) wing is performed to obtain the optimal anti-symmetric wing twist distribution for the roll control of the MAV's wing instead of using conventional ailerons. This twist distribution should produce minimum induced drag and achieve a better roll response. The implementation of several anti-symmetric load distributions such as the half lemniscates and the Horten distributions is studied leading to an initial solution for the optimal distribution that could achieve better roll requirements. Multhopp's method based on Prandtl's classical lifting line theory is used for the determination of the spanwise load distribution required during the optimisation process. The optimisation process is based on the modified feasible directions gradient based optimisation algorithm implemented in the optimisation system, VisualDOC, given by Dr. Garret Vanderplaats. The proposed optimisation process is applied to the 'BARQ'developed MAV which has successful flight in July 2009. NOMENCLATUREa o aerofoil lift curve slope (rad -1 ) AR aspect ratio
PurposeThe purpose of this paper is to show the merit of using mission information in tuning the controller gains for Stewart manipulator instead of the generic inputs previously developed in literature.Design/methodology/approachThe paper introduces two optimization techniques based on mission information. The first technique, a partial‐information technique, uses gain scheduling that applies different controllers for different mission tracks. The second technique, a full‐information technique uses a single robust controller by considering the full mission data. For demonstrating these techniques' feasibility, a nonlinear numerical simulation for a Stewart manipulator was built and tested using a generic mission. This mission consists of two piecewise trajectories (tracks). The proposed techniques were compared with one of the previous optimization techniques in literature, no‐information technique, in which a step response is used to search for optimal controller gains without any information about the mission. Genetic algorithms were used to search for the optimal controller gain in each case with different cost functions.FindingsBased on the numerical simulations, the proposed mission‐based optimization techniques have superior performances compared with no‐information technique.Research limitations/implicationsThe proposed techniques were applied in a joint space or for a decentralized control. The work can be extended to be applied in a task space or for a centralized control.Originality/valueThe paper proposes two novel optimization techniques: partial‐ and full‐information techniques for tuning the controller gains of a Stewart manipulator, where mission information was imbedded into the cost function. These two techniques are generally applicable for other nonlinear systems such as aircraft stability and control augmentation systems.
This paper proposes designing a static output feedback controller for a structural-acoustics coupling system using piezoelectric actuators. The system consists of a rectangular cavity with two flexible plates, one at the top of the cavity and the other at the bottom, and four other rigid boundaries. Piezoelectric pair patches are considered to be bonded to the top plate, and each pair is assumed to produce a pure moment actuation. The top plate is exposed to an external pressure excitation due to a planar wave generated by a sound source mounted above the cavity. The series solution is assumed for the displacements of the plates and the pressure inside the cavity. The responses of the coupled system are obtained using Galarkin’s method. In the control scheme, the controller gains have been optimally tuned using genetic algorithms. The proposed static output feedback controller shows an acceptable performance with simple implementation requirements compared to the linear quadratic Gaussian state feedback controller.
The aerodynamic shape optimisation of a micro air vehicle (MAV) wing is performed to obtain the basic wing geometrical characteristics which produce the maximum range and endurance requirements. Multhopp's method based on Prandtl's classical lifting line theory is used for the determination of the spanwise load distribution required during the optimisation process. The obtained lift and drag characteristics are used for the derivation of the range and endurance equations of an electrically driven micro air vehicle. The optimisation process is based on the modified feasible directions gradient based optimisation algorithm. Results are validated using wind tunnel measurements showing very good agreement. Results are also compared with solutions to the Navier-Stokes equations obtained with ANSYS-CFX finite elements using different turbulence models. These include the k-ε and the shear stress transport (SST) models as well as the Reynolds stress model.
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