A rocket-borne electrostatic analyzer for measurement of energetic particle flux

A rocket-borne electrostatic analyzer experiment is described. It is used to measure energetic particle flux (0.9 to 14 keV) in the nighttime midlatitude E region. Energetic particle precipitation is believed to be a significant nighttime ionization source, particularly during times of high geomagnetic activity. The experiment was designed for use in the payload of a Nike Apache sounding rocket. The electrostatic analyzer employs two cylindrical parallel plates subtending a central angle of 90 deg. The voltage waveform supplied to the plates is a series of steps synchronized to the spin of the payload during flight. Both positive and negative voltages are provided, extending the detection capabilities of the instrument to both electrons and protons (and positive ions). The development, construction and operation of the instrument is described together with a preliminary evaluation of its performance in a rocket flight

Feedback MPC for Torque-Controlled Legged Robots

The computational power of mobile robots is currently insufficient to achieve torque level whole-body Model Predictive Control (MPC) at the update rates required for complex dynamic systems such as legged robots. This problem is commonly circumvented by using a fast tracking controller to compensate for model errors between updates. In this work, we show that the feedback policy from a Differential Dynamic Programming (DDP) based MPC algorithm is a viable alternative to bridge the gap between the low MPC update rate and the actuation command rate. We propose to augment the DDP approach with a relaxed barrier function to address inequality constraints arising from the friction cone. A frequency-dependent cost function is used to reduce the sensitivity to high-frequency model errors and actuator bandwidth limits. We demonstrate that our approach can find stable locomotion policies for the torque-controlled quadruped, ANYmal, both in simulation and on hardware.Comment: Paper accepted to IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019

Requirements Study for System Implementation of an Atmospheric Laser Propagation Experiment Program, Volume II

Program planning, ground support and airborne equipment for laser space communication syste

Automatic Construction of Predictive Neuron Models through Large Scale Assimilation of Electrophysiological Data.

We report on the construction of neuron models by assimilating electrophysiological data with large-scale constrained nonlinear optimization. The method implements interior point line parameter search to determine parameters from the responses to intracellular current injections of zebra finch HVC neurons. We incorporated these parameters into a nine ionic channel conductance model to obtain completed models which we then use to predict the state of the neuron under arbitrary current stimulation. Each model was validated by successfully predicting the dynamics of the membrane potential induced by 20-50 different current protocols. The dispersion of parameters extracted from different assimilation windows was studied. Differences in constraints from current protocols, stochastic variability in neuron output, and noise behave as a residual temperature which broadens the global minimum of the objective function to an ellipsoid domain whose principal axes follow an exponentially decaying distribution. The maximum likelihood expectation of extracted parameters was found to provide an excellent approximation of the global minimum and yields highly consistent kinetics for both neurons studied. Large scale assimilation absorbs the intrinsic variability of electrophysiological data over wide assimilation windows. It builds models in an automatic manner treating all data as equal quantities and requiring minimal additional insight