The development of the CubeSat standard, a picosatellite standard, has become a tool that encourages engineering collaboration, trains students with real-world satellite experience, and provides technology advancement in the aerospace industry. The Poly-Picosatellite Orbital Deployer (P-POD), in conjuction with the CubeSat standard, plays a key role in providing access to space for CubeSats. Developing satellites at the CubeSat level highlight the increasing opportunities for access to space while yielding quicker development times. The upcoming launches demonstrate the growing interest of universities, companies, and government organizations to develop CubeSats to perform valuable scientific experiments and missions. The educational benefits of CubeSat development are emphasized by providing an ideal training ground for future scientists and engineers.

The X-56A aircraft is a remotely-piloted aircraft with flutter modes intentionally designed into the flight envelope. The X-56A program must demonstrate flight control while suppressing all unstable modes. A previous X-56A model study demonstrated a distributed-sensing-based active shape and active flutter suppression controller. The controller relies on an estimator which is sensitive to bias. This estimator is improved herein, and a real-time robust estimator is derived and demonstrated on 1530 fiber optic sensors. It is shown in simulation that the estimator can simultaneously reject 230 worst-case fiber optic sensor failures automatically. These sensor failures include locations with high leverage (or importance). To reduce the impact of leverage outliers, concentration based on a Mahalanobis trim criterion is introduced. A redescending M-estimator with Tukey bisquare weights is used to improve location and dispersion estimates within each concentration step in the presence of asymmetry (or leverage). A dynamic simulation is used to compare the concentrated robust estimator to a state-of-the-art real-time robust multivariate estimator. The estimators support a previously-derived mu-optimal shape controller. It is found that during the failure scenario, the concentrated modal estimator keeps the system stable. Nomenclature= maximum desired strain variation on sensors upstream of the fiber break = bias on sensor near the fiber break = current M-step = number of M-steps = current concentration step = number of concentration steps = squared Mahalanobis distance ( ) = squared Mahalanobis distance of the argument = upper bound of ( ) = deformations defined at time = reference deformations = finite residuals of all sensors = finite residual of the sensor = plant = tuning constant for weight function of M-estimator = controller = sensor station 1 Aerospace Engineer, PhD; NASA Armstrong, Controls Branch / 4840D; AIAA Member; peter.m.suh@nasa.gov. 2 Aerospace Engineer; NASA Armstrong, Aerostructures Branch / 4820; alexander.w.chin@nasa.gov. 2 = index of SFOS used for sensor feedback = row index vector of for reference deformations ( ) = median of the argument = number of mode shapes retained in the model = column index vector of for reference modal displacements = number of nodes in finite element model ( ) = normal distribution with argument parameters = airframe sensor noise = bias induced by simulated FOS failure on sensors = simulated fiber optic sensor noise = tuning constant ( ) = position of fiber break ( ) = positions of sensors in a radius upstream of the fiber break ( ) = vector of modal displacements at time ( ) = modal displacement at time ̂ = estimated modal displacements ̂( ) = estimated modal displacements at time step ( ) = modal displacement references = radius of sensors affected near the fiber break = set of all sensors = set of good sensors in concentration step = set of sensors upstream and near the fiber break ̂ ( ) = SFOS strain measurements ( ) = measured strain of sensor after the fiber break (...

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The Dynamic Inertia Measurement (DIM) method uses a ground vibration test setup to determine the mass properties of an object using information from frequency response functions. Most conventional mass properties testing involves using spin tables or pendulum-based swing tests, which for large aerospace vehicles becomes increasingly difficult and time-consuming, and therefore expensive, to perform. The DIM method has been validated on small test articles but has not been successfully proven on large aerospace vehicles. In response, the National Aeronautics and Space Administration Armstrong Flight Research Center (Edwards, California) conducted mass properties testing on an "iron bird" test article that is comparable in mass and scale to a fighter-type aircraft. The simple two-I-beam design of the "iron bird" was selected to ensure accurate analytical mass properties. Traditional swing testing was also performed to compare the level of effort, amount of resources, and quality of data with the DIM method. The DIM test showed favorable results for the center of gravity and moments of inertia; however, the products of inertia showed disagreement with analytical predictions. KEYWORDS: dynamic inertia measurement frequency response function ground vibration test mass properties moment of inertia Nomenclature AFRC = Armstrong Flight Research Center CAD = computer-aided design CG = center of gravity CRV = Crew Return Vehicle DFRC = Dryden Flight Research Center diff = difference DIM = dynamic inertia measurement DOF = degree of freedom ETA = engineering test article F = force FRF = frequency response function