SCIENTIFIC AND TECHNICAL AEROSPACE REPORTS

A Biweekly Publication of the National Aeronautics and Space Administration
VOLUME 44, ISSUE 13 - JULY 5, 2006

13 ASTRODYNAMICS
Includes powered and free flight trajectories; orbital and launching dynamics.

20060017647 Naval Postgraduate School, Monterey, CA USA

Optimal Trajectory Reconfiguration and Retargeting for a Reusable Launch Vehicle

Shaffer, Patrick J; Ross, I M; Oppenheimer, Michael W; Doman, David B; Aug 2005; 15 pp.; In English; Original contains color illustrations Report No.(s): AD-A445135; No Copyright; Avail.: CASI: A03, Hardcopy

Autonomous reusable launch vehicles (RLV) are being pursued as low-cost alternatives to expendable launch vehicles and the Shuttle. The employment of autonomous reusable launch vehicles requires additional guidance and control robustness to fulfill the role of an adaptive human pilot, in the event of failures or unanticipated conditions. The guidance and control of these vehicles mandate new guidance strategies that are able to identify and adapt to vehicle failures during the flight and still return to earth safely. This work utilizes an online trim algorithm that provides the outer loop with the feasible range of Mach number and angle of attack, for which the vehicle can be rotationally trimmed. The algorithm allows one to include 6-degree-of-freedom (DOF) trim effects and constraints in a reduced order dynamical model which is used in the solution of an optimal control problem. A direct pseudospectral method is used to solve a two-point-boundary-value problem which determines the optimal entry trajectory subject to appropriate constraints such as normal load, dynamic pressure limits, heat load limits, and state dependent constraints. DTIC

Aerodynamic Characteristics; Launch Vehicles; Reusable Launch Vehicles; Trajectories

20060017681 Alcatel Space Industries, Cannes la Bocca, France

Magnetic Actuator in Space and Application for High Precision Formation Flying

Dargent, Thierry; Maini, Massimiliano; Jul 13, 2005; 10 pp.; In English Report No.(s): AD-A445193; No Copyright; Avail.: CASI: A02, Hardcopy

Electromagnetic (EM) actuators in space applications are not a new idea but they are most of the time associated to low Earth orbit missions, where the on-board magnetic moment interacts with the Earth magnetic field. More recently EM actuators have been studied in the context of formation flying as a way to generate inter-spacecraft force and torque in order to control the formation geometry (relative position and attitude of the spacecrafts). With respect to other possible actuators (like FEEP and cold-gas thrusters), EM actuators do not require propellant (hence ensuring longer lifetime) and do not cause contamination problems, but are effective only in a limited range of distances (due to the magnetic field decreasing as 1/r(exp 3)).

In this paper we consider a setup where a spacecraft (called the hub) generates the magnetic field while the other spacecrafts (called the flyers) modify their on-board magnetic moment in order to generate the desired force and torque. We show that if the magnetic field generated by the hub is constant it is not possible to generate any combination of force and torque (there are forbidden directions, independently of the magnitude of the desired force and torque) and we present a possible solution based on a rotating field. The main idea is to have a time-varying magnetic field generated by the hub (chosen as a rotating magnetic dipole) and time-varying magnetic moments on the flyers. The variation law on each flyer's magnetic moment is computed in order to obtain the desired force and torque on average (over one rotation period).With such approach, the instantaneous force and torque may differ from the desired ones, these discrepancies being 'filtered out' by the spacecraft inertia. DTIC

Actuators; Aerospace Engineering; Electromagnetic Properties; Formation Flying

20060018836 Maryland Univ., College Park, MD USA

Sampling Effects on Trajectory Learning and Production

Lin, Daw-Tung; Dayhoff, Judith E; Jan 1994; 19 pp.; In English Contract(s)/Grant(s): N00014-90-K-2010; NSF-CDR-88-03012 Report No.(s): AD-A445783; ISR-TR-95-7; No Copyright; Avail.: CASI: A03, Hardcopy

The time-delay neural network (TDNN) and the adaptive time-delay neural network (ATNN) are effective tools for signal production and trajectory generation. Previous studies have shown production of circular and figure-eight trajectories to be robust after training. This report shows the effects of different sampling rates on the production of trajectories by the ATNN neural network, including the influence of sampling rate on the robustness and noise-resilience of the resulting system. Although fast training occurred with few samples per trajectory, and the trajectory was learned successfully, more resilience to noise was observed when there were higher numbers of samples per trajectory. The effects of changing the initial segments that begin the trajectory generation were evaluated.

This evaluation showed that a minimum length of initial segment is required, but that the location of that segment does not influence the trajectory generation, even when different initial segments are used during training and recall. A major conclusion from these results is that the network learns the inherent features of the trajectory rather than memorizing each point. When a recurrent loop was added from the output to the input of the ATNN, the training was shown to result in an attractor of the network for a figure-eight trajectory, which involves more complexity due to crossover compared with previous attractor training of a circular trajectory. Furthermore, when the trajectory length was not a multiple of the sampling interval, the trained network generated intervening points on subsequent repetitions of the trajectory, a feature of limit cycle attractors observed in dynamic networks. Thus, an effective method of training an indvidual dynamic attractor into a neural network is extended to more complex trajectories and to show the properties of a limit cycle attractor. DTIC

Adaptation; Circles (Geometry); Education; Neural Nets; Sampling; Signal Processing; Time Lag; Trajectories

Source: NASA