Influence of ground effect on longitudinal aerodynamic damping of wing-in-ground effect vehicles

Shabarov, V. V.; Kaliasov, Pavel; Peplin, Fedor

doi:10.1080/09377255.2020.1724647

Cited by 6 publications

(1 citation statement)

References 12 publications

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“…In [ 17 ] authors study the cross influence of different design parameters and the flight stability of a WIG craft. In [ 18 ] the author present a thorough study of flight dynamics of WIG crafts, followed by an analysis of performance and stability referred to different equilibrium conditions, making broad use of numerical calculations to study the equations of motion; in [ 19 ] authors introduce a method for computing unsteady aerodynamic derivatives using Reynolds-averaged Navier–Stokes simulations, and they use these results to study their effect on longitudinal stability. In [ 20 ] the author present a detailed study on the longitudinal stability of a standard NACA4412 airfoil, whose mean camber line has been previously modified giving it a partially flat lower surface to achieve an S-shaped profile in order to improve its height stability.…”

Section: Previous Workmentioning

confidence: 99%

Nonlinear Control Strategies for an Autonomous Wing-In-Ground-Effect Vehicle

Patria

Rossi

Fernández

et al. 2021

Sensors

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Autonomous vehicles are nowadays one of the most important technologies that will be incorporated to every day life in the next few years. One of the most promising kind of vehicles in terms of efficiency and sustainability are those known as Wing-in-Ground crafts, or WIG crafts, a family of vehicles that seize the proximity of ground to achieve a flight with low drag and high lift. However, this kind of crafts lacks of a sound theory of flight that can lead to robust control solutions that guarantees safe autonomous operation in all the cruising phases.In this paper we address the problem of controlling a WIG craft in different scenarios and using different control strategies in order to compare their performance. The tested scenarios include obstacle avoidance by fly over and recovering from a random disturbance in vehicle attitude. MPC (Model Predictive Control) is tested on the complete nonlinear model, while PID, used as baseline controller, LQR (Linear Quadratic Regulator) and adaptive LQR are tested on top of a partial feedback linearization. Results show that LQR has got the best overall performance, although it is seen that different design specifications could lead to the selection of one controller or another.

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