Funded by the NSF CubeSat and NASA ELaNa programs, the Dynamic Ionosphere CubeSat Experiment (DICE) mission consists of two 1.5U CubeSats which were launched into an eccentric low Earth orbit on October 28, 2011. Each identical spacecraft carries two Langmuir probes to measure ionospheric in-situ plasma densities, electric field probes to measure in-situ DC and AC electric fields, and a science grade magnetometer to measure in-situ DC and AC magnetic fields. Given the tight integration of these multiple sensors with the CubeSat platforms, each of the DICE spacecraft is effectively a "sensorsat" capable of comprehensive ionospheric diagnostics. The use of two identical sensor-sats at slightly different orbiting velocities in nearly identical orbits permits the de-convolution of spatial and temporal ambiguities in the observations of the ionosphere from a moving platform. In addition to demonstrating nanosat-based constellation science, the DICE mission is advancing a number of groundbreaking CubeSat technologies including miniaturized mechanisms and high-speed downlink communications.

This paper considers boundary control of time fractional order diffusion-wave equation. Both boundary stabilization and disturbance rejection are considered. This paper, for the first time, has confirmed, via hybrid symbolic and numerical simulation studies, that the existing two schemes for boundary stabilization and disturbance rejection for (integer order) wave/beam equations are still valid for time fractional order diffusion-wave equations. The problem definition, the hybrid symbolic and numerical simulation techniques, outlines of the methods for boundary stabilization and disturbance rejection are presented together with extensive simulation results. Different dynamic behaviors are revealed for different fractional orders which may stimulate new future research opportunities.

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A training method for a class of neural network controllers is presented which guarantees closed-loop system stability. The controllers are assumed to be nonlinear, feedforward, sampled-data, full-state regulators implemented as single hidden-layer neural networks. The controlled systems must be locally hermitian and observable. Stability of the closed-loop system is demonstrated by determining a Lyapunov function, which can be used to identify a finite stability region about the regulator point.