Experimental trapped vorticity flight control using an augmenting error minimization adaptive law


Muse J. A. , Kutay A. T. , Calise A. J.

AIAA Guidance, Navigation and Control Conference and Exhibit, 2008 (Diğer Kurumların Hakemli Dergileri) identifier

  • Cilt numarası:
  • Basım Tarihi: 2008
  • Doi Numarası: 10.2514/6.2008-6962
  • Dergi Adı: AIAA Guidance, Navigation and Control Conference and Exhibit

Özet

Closed-loop feedback control is used in a series of wind tunnel experiments to effect commanded 2-DOF maneuvers (pitch and plunge) of a free airfoil without moving control surfaces. The objective is to achieve control bandwidths that are beyond those achievable with mechanical control surfaces. Bi-directional changes in the pitching moment over a range of angles of attack are effected by controllable, nominally-symmetric, trapped vorticity concentrations on both the suction and pressure surfaces near the trailing edge. Actuation is applied on both surfaces by hybrid actuators that are each comprised of a miniature obstruction integrated with a synthetic jet actuator to manipulate and regulate the vorticity concentrations. In the present work, the model is trimmed using position and attitude feedback loops that are actuated by servo motors and a ball screw mechanism in the plunge axis. Once the model is trimmed, the position feedback loop in the plunge axis is opened and the plunge axis is controlled in force mode. Force mode allows the simulation of free flight in the wind tunnel. It can maintain the static trim force on the model, alter its effective mass, change the dynamic characteristics of the model, and introduce disturbances. Attitude stabilization and plunge position control of the model is achieved by closing the position loop through the flow control actuators using a model reference adaptive controller designed to maintain a specified level of tracking performance in the presence of disturbances, parametric uncertainties and unmodeled dynamics associated with the flow. The control law employs a neural network adaptive element in which the adaptation law is based on a novel error minimization scheme. The adaptive element augments a linear control law, and the closed loop system can be shown to be uniformly ultimately bounded through a Lyapunov-like stability analysis. © 2008 by the American Institute of Aeronautics and Astronautics, Inc.