Ali T. Kutay

Validation of A Low-Order Model for Closed Loop Flow Control Enabled Flight

A simple low-order model is derived to determine the flow forces and moments on an airfoil that arbitrarily pitches and plunges with the presence of synthetic jet actuation for the use in an adaptive closed-loop control scheme. The low-order model captures the attached flow response of an airfoil in the presence of synthetic jet actuators near the trailing edge. The model includes two explicit non-linear states for fluid variables and can be easily coupled to the rigid body dynamics of the system. The model is validated with high fidelity numerical simulations and experiments. The low-order model agreement with experiments is good for low reduced frequency pitching. The agreement to numerical simulations is also good for reduced frequencies that are an order of magnitude higher than those attainable in experiments.

A 1-DOF Wind Tunnel Experiment in Adaptive Flow Control

This paper presents an adaptive control approach for pitch control of a one degree-of- freedom wing using imbedded flow control actuators. Such actuators offer a unique opportunity for rapid maneuvering and gust rejection with low power consumption. The proposed adaptive non-model based control approach is motivated by the difficulty in constructing a reasonably accurate physical model of active flow control actuators. In addition, during dynamic maneuvers, possible interactions between unsteady fluid dynamics and vehicle dynamics introduce additional uncertainty. The control architecture employs a neural network based adaptive element that permits adaptation to both parametric uncertainty and unmodeled dynamics. Wind tunnel experimental results and controller implementation details are presented.

Distributed Adaptive Output Feedback Control Design and Application to a Formation Flight Experiment

An approach for augmenting existing distributed controller designs for large-scale interconnected systems with neural network based adaptive elements is proposed. It is assumed that the controllers are interconnected in the same way as the plant and based on the available measurements, a single hidden layer neural network is introduced for each subsystem to partially cancel the effects of the sub-system interconnections and modeling errors on tracking performance. Boundedness of error signals is shown through Lyapunov’s direct method. Effectiveness of the approach is demonstrated on a wind tunnel experiment representing aerial vehicles flying in a highly aerodynamically coupled formation.

An Adaptive Guidance Approach For Spinning Projectiles

*† ‡ § 1 has shown a significant potential for improving the accuracy of direct fire spinning projectiles through the implementation of aerodynamic control. A guidance law optimized for this application was developed. Further research has shown that the guidance law cannot adjust quickly enough to accommodate actuator nonlinearities and uncertainties. Of particular interest is the use of miniature synthetic jets, which achieve a control force by interacting with the flow. The full model of such actuators might not be completely known leading to decreased controlled system performance. Actuator induced moment effect causes amplification or attenuation of the control force depending on the moment direction causing increased target misses and peak control force. Wind disturbances are expected to cause further performance degradation. We describe and provide a preliminary evaluation of a method of dealing with these problems by augmenting the guidance law with an adaptive controller.

DYNAMIC FLIGHT MANEUVERING USING TRAPPED VORTICITY FLOW CONTROL

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. 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 [O(0.01c)] 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 so to maintain the static trim force on the model, and alter its effective mass. Meanwhile the servomotor in the pitch axis is only used to alter the dynamic characteristics of the model in pitch, and to introduce disturbances. Attitude stabilization and 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 controller employs a neural network based adaptive element and adaptation laws derived by a Lyapunov-like stability analysis of the closed loop system.

A Closed-Loop Flight Control Experiment using Active Flow Control Actuators

Closed-loop pitch control on a moving 1-DOF wing model is investigated in wind tunnel experiments. The models attitude is controlled over a broad range of angles of attack when the baseline flow is fully attached using bi-directional pitching moment that is effected by flow-controlled trapped vorticity concentrations on the pressure and suction surfaces near the trailing edge. In the present work, the model is trimmed using a position feedback loop and a servomotor actuator. Once the model is trimmed, the position feedback loop is opened and the servomotor acts like an inner loop control to alter the dynamic characteristics and to introduce disturbances. Position control of the model is achieved by the flow control actuation using an arbitrary reference model based adaptive outer loop controller. The control architecture employs a neural network based adaptive element that permits adaptation to both parametric uncertainty and unmodeled dynamics.

Adaptive output feedback control with reduced sensitivity to sensor noise

We address adaptive output feedback control of uncertain nonlinear systems with noisy output measurements, in which both the dynamics and the dimension of the regulated systems may be unknown, and only the relative degree of the regulated output is assumed to be known. Given a smooth reference trajectory, the problem is to design a controller that forces the system measurement to track it with bounded errors. A recently developed method proposes the use of a linear error observer that estimates the tracking error and its derivatives. Since the observer is full order, it also estimates the states of the controller, even though these states are exactly known. It has been observed experimentally that the resulting adaptive control architecture is very sensitive to sensor noise. In this paper we provide a specific reduced order observer that significantly reduces sensitivity to sensor noise in adaptive control design. Experimental results on a three degrees of freedom laboratory model helicopter are used to illustrate the effectiveness of the reduced order observer design.

Adaptive Longitudinal Control of Aircraft Using Synthetic Jets

This paper presents an adaptive control approach for controlling the longitudinal dynamics of a generic lifting surface using imbedded flow control actuators. Such actuators offer a unique opportunity for rapid maneuvering and gust rejection with low power consumption. The model’s state is controlled over a broad range of angles of attack and model dynamic characteristics when the baseline flow is fully attached using bi-directional pitching moment that is effected by flow-controlled trapped vorticity concentrations on the pressure and suction surfaces near the trailing edge of the lifting surface. The proposed adaptive non-model based control approach is motivated by the difficulty in constructing a reasonably accurate physical model of active flow control actuators. In addition, during dynamic maneuvers, possible interactions between unsteady fluid dynamics and vehicle dynamics introduce additional uncertainty. System modeling uses a neural network based architecture to capture the non-linearities of the flow actuation process. This model is employed for the simulation studies performed in this paper. The control architecture employs a neural network based adaptive element that permits adaptation to both parametric uncertainty and unmodeled dynamics. This paper represents an extension of the authors’ previous efforts to control a wing in pitch using synthetic jets. In the current design, the variable dynamic properties of the model are achieved in a wind tunnel using a novel traverse based on force control. The basic force control concept is addressed and simulations for longitudinal control are provided.

Experimental Trapped Vorticity Flight Control Using An Augmenting Error Minimization Adaptive Law

Closed-loop feedback control is used in a series of wind tunnel experiments to efiect 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 efiected 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 ∞ight in the wind tunnel. It can maintain the static trim force on the model, alter its efiective 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 ∞ow control actuators using a model reference adaptive controller designed to maintain a specifled level of tracking performance in the presence of disturbances, parametric uncertainties and unmodeled dynamics associated with the ∞ow. 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.

A Novel Force Control Traverse for Simulating UAV Flight In A Wind Tunnel

Wind tunnel testing methods often infer aerodynamic performance via quasi-steady predictions, driving a wind tunnel model through predetermined trajectories, or quasisteady integration of the applied aerodynamic forces and moments. This paper focuses on a new testing approach that allows the simulation and study of the true longitudinal dynamics of a wind tunnel model. The system design allows an experimentalist to carry heavy models with all of the needed sensors for evaluating true unsteady aerodynamic ∞ight qualities. Controlling the forces and moments applied to the model allows the experimentalist to remove efiects such as gravity and traverse friction. Using feedback on the applied force and moments, the dynamic characteristics of the model can be actively modifled to alter dynamic characteristics such as the system’s natural frequency, damping, c.g. location, and/or efiective model inertia. All dynamic efiects can be set arbitrarily but must be within traverse limits. The model does not have to be balanced because the control system actively restricts motion to be within the longitudinal plane. An example experimental simulation ∞ight is shown using a NACA 4415 wing section with a servo motor in torque mode to simulate the efiect of a conventional tail surface.