This article presents a completely servo-less, piezoelectric controlled, wind tunnel and flight tested, remotely piloted aircraft that has been developed by the 2010 Virginia Tech Wing Morphing Design Team (a senior design project between the Departments of Mechanical Engineering and Aerospace and Ocean Engineering). A type of piezocomposite actuator, the Macro-Fiber Composite, is used for changing the camber of all control surfaces on the aircraft. The aircraft is analyzed theoretically for its aerodynamic characteristics to aid the design of the piezoelectric control surfaces. A vortex lattice analysis complemented the database of aerodynamic derivatives used to analyze control response. Steady-state roll rates were measured in a wind tunnel and were compared to a similar aircraft with servomotor actuated control surfaces. The theoretical analysis and wind tunnel testing demonstrated the stability and control authority of the concept, culminating in the first flight of the completely Macro-Fiber Composite controlled aircraft on 29 April 2010. An electric motor-driven propulsion system is used to generate thrust, and all systems are powered with a single lithium polymer battery. This vehicle became the first completely Macro-Fiber Composite controlled, flight tested aircraft. It is also known to be the first fully solid-state piezoelectric material controlled, nontethered, flight tested fixed-wing aircraft.

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The control of mobile sensor networks uses sensor measurements to update a model of an unknown or estimated process, which in turn guides the collection of subsequent measurements—a feedback control framework called adaptive sampling. Applications for adaptive sampling exist in a wide range of settings, especially for unmanned or autonomous vehicles that can be deployed cheaply and in cooperative groups. The dynamics of mobile sensor platforms are often simplified to planar self-propelled particles subject to the ambient flow of the surrounding fluid. Sensor measurements are assimilated into continuous or discrete models of the process of interest, which in general can vary in space and time. The variability of the estimated process is one metric to score future candidate sampling trajectories, along with information- and uncertainty-based metrics. Sampling tasks are allocated to the network using centralized or decentralized optimization, in order to avoid redundant measurements and observational gaps.