State Estimation for Kite Power Systems with Delayed Sensor Measurements

We present a novel estimation approach for airborne wind energy systems with ground-based control and energy generation. The estimator fuses measurements from an inertial measurement unit attached to a tethered wing and position measurements from a camera as well as line angle sensors in an unscented Kalman filter. We have developed a novel kinematic description for tethered wings to specifically address tether dynamics. The presented approach simultaneously estimates feedback variables for a flight controller as well as model parameters, such as a time-varying delay. We demonstrate the performance of the estimator for experimental flight data and compare it to a state-of-the-art estimator based on inertial measurements

A nanoradian differential VLBI tracking demonstration

The shift due to Jovian gravitational deflection in the apparent angular position of the radio source P 0201+113 was measured with very long baseline interferometry (VLBI) to demonstrate a differential angular tracking technique with nanoradian accuracy. The raypath of the radio source P 0201+113 passed within 1 mrad of Jupiter (approximately 10 Jovian radii) on 21 Mar. 1988. Its angular position was measured 10 times over 4 hours on that date, with a similar measurement set on 2 Apr. 1988, to track the differential angular gravitational deflection of the raypath. According to general relativity, the expected gravitational bend of the raypath averaged over the duration of the March experiment was approximately 1.45 nrad projected onto the two California-Australia baselines over which it was measured. Measurement accuracies on the order of 0.78 nrad were obtained for each of the ten differential measurements. The chi(exp 2) per degree of freedom of the data for the hypothesis of general relativity was 0.6, which suggests that the modeled dominant errors due to system noise and tropospheric fluctuations fully accounted for the scatter in the measured angular deflections. The chi(exp 2) per degree of freedom for the hypothesis of no gravitational deflection by Jupiter was 4.1, which rejects the no-deflection hypothesis with greater than 99.999 percent confidence. The system noise contributed about 0.34 nrad per combined-baseline differential measurement and tropospheric fluctuations contributed about 0.70 nrad. Unmodeled errors were assessed, which could potentially increase the 0.78 nrad error by about 8 percent. The above chi(exp 2) values, which result from the full accounting of errors, suggest that the nanoradian gravitational deflection signature was successfully tracked

Simulating a dual beam combiner at SUSI for narrow-angle astrometry

The Sydney University Stellar Interferometer (SUSI) has two beam combiners, i.e. the Precision Astronomical Visible Observations (PAVO) and the Microarcsecond University of Sydney Companion Astrometry (MUSCA). The primary beam combiner, PAVO, can be operated independently and is typically used to measure properties of binary stars of less than 50 milliarc- sec (mas) separation and the angular diameters of single stars. On the other hand, MUSCA was recently installed and must be used in tandem with the for- mer. It is dedicated for microarcsecond precision narrow-angle astrometry of close binary stars. The performance evaluation and development of the data reduction pipeline for the new setup was assisted by an in-house computer simulation tool developed for this and related purposes. This paper describes the framework of the simulation tool, simulations carried out to evaluate the performance of each beam combiner and the expected astrometric precision of the dual beam combiner setup, both at SUSI and possible future sites.Comment: 28 pages, 23 figures, accepted for publication in Experimental Astronomy. The final publication is available at http://link.springer.co

The performance of arm locking in LISA

For the laser interferometer space antenna (LISA) to reach it's design sensitivity, the coupling of the free running laser frequency noise to the signal readout must be reduced by more than 14 orders of magnitude. One technique employed to reduce the laser frequency noise will be arm locking, where the laser frequency is locked to the LISA arm length. This paper details an implementation of arm locking, studies orbital effects, the impact of errors in the Doppler knowledge, and noise limits. The noise performance of arm locking is calculated with the inclusion of the dominant expected noise sources: ultra stable oscillator (clock) noise, spacecraft motion, and shot noise. Studying these issues reveals that although dual arm locking [A. Sutton & D. A Shaddock, Phys. Rev. D 78, 082001 (2008).] has advantages over single (or common) arm locking in terms of allowing high gain, it has disadvantages in both laser frequency pulling and noise performance. We address this by proposing a hybrid sensor, retaining the benefits of common and dual arm locking sensors. We present a detailed design of an arm locking controller and perform an analysis of the expected performance when used with and without laser pre-stabilization. We observe that the sensor phase changes beneficially near unity-gain frequencies of the arm-locking controller, allowing a factor of 10 more gain than previously believed, without degrading stability. We show that the LISA frequency noise goal can be realized with arm locking and Time-Delay Interferometry only, without any form of pre-stabilization.Comment: 28 pages, 36 figure

The Power of Online Learning in Stochastic Network Optimization

In this paper, we investigate the power of online learning in stochastic network optimization with unknown system statistics {\it a priori}. We are interested in understanding how information and learning can be efficiently incorporated into system control techniques, and what are the fundamental benefits of doing so. We propose two \emph{Online Learning-Aided Control} techniques, $\mathtt{OLAC}$ and $\mathtt{OLAC2}$, that explicitly utilize the past system information in current system control via a learning procedure called \emph{dual learning}. We prove strong performance guarantees of the proposed algorithms: $\mathtt{OLAC}$ and $\mathtt{OLAC2}$ achieve the near-optimal $[O(\epsilon), O([\log(1/\epsilon)]^2)]$ utility-delay tradeoff and $\mathtt{OLAC2}$ possesses an $O(\epsilon^{-2/3})$ convergence time. $\mathtt{OLAC}$ and $\mathtt{OLAC2}$ are probably the first algorithms that simultaneously possess explicit near-optimal delay guarantee and sub-linear convergence time. Simulation results also confirm the superior performance of the proposed algorithms in practice. To the best of our knowledge, our attempt is the first to explicitly incorporate online learning into stochastic network optimization and to demonstrate its power in both theory and practice

The Power of Online Learning in Stochastic Network Optimization

In this paper, we investigate the power of online learning in stochastic network optimization with unknown system statistics {\it a priori}. We are interested in understanding how information and learning can be efficiently incorporated into system control techniques, and what are the fundamental benefits of doing so. We propose two \emph{Online Learning-Aided Control} techniques, $\mathtt{OLAC}$ and $\mathtt{OLAC2}$, that explicitly utilize the past system information in current system control via a learning procedure called \emph{dual learning}. We prove strong performance guarantees of the proposed algorithms: $\mathtt{OLAC}$ and $\mathtt{OLAC2}$ achieve the near-optimal $[O(\epsilon), O([\log(1/\epsilon)]^2)]$ utility-delay tradeoff and $\mathtt{OLAC2}$ possesses an $O(\epsilon^{-2/3})$ convergence time. $\mathtt{OLAC}$ and $\mathtt{OLAC2}$ are probably the first algorithms that simultaneously possess explicit near-optimal delay guarantee and sub-linear convergence time. Simulation results also confirm the superior performance of the proposed algorithms in practice. To the best of our knowledge, our attempt is the first to explicitly incorporate online learning into stochastic network optimization and to demonstrate its power in both theory and practice

Optimal Real-time Spectrum Sharing between Cooperative Relay and Ad-hoc Networks

Optimization based spectrum sharing strategies have been widely studied. However, these strategies usually require a great amount of real-time computation and significant signaling delay, and thus are hard to be fulfilled in practical scenarios. This paper investigates optimal real-time spectrum sharing between a cooperative relay network (CRN) and a nearby ad-hoc network. Specifically, we optimize the spectrum access and resource allocation strategies of the CRN so that the average traffic collision time between the two networks can be minimized while maintaining a required throughput for the CRN. The development is first for a frame-level setting, and then is extended to an ergodic setting. For the latter setting, we propose an appealing optimal real-time spectrum sharing strategy via Lagrangian dual optimization. The proposed method only involves a small amount of real-time computation and negligible control delay, and thus is suitable for practical implementations. Simulation results are presented to demonstrate the efficiency of the proposed strategies.Comment: One typo in the caption of Figure 5 is correcte

The Value-of-Information in Matching with Queues

We consider the problem of \emph{optimal matching with queues} in dynamic systems and investigate the value-of-information. In such systems, the operators match tasks and resources stored in queues, with the objective of maximizing the system utility of the matching reward profile, minus the average matching cost. This problem appears in many practical systems and the main challenges are the no-underflow constraints, and the lack of matching-reward information and system dynamics statistics. We develop two online matching algorithms: Learning-aided Reward optimAl Matching ($\mathtt{LRAM}$) and Dual-$\mathtt{LRAM}$ ($\mathtt{DRAM}$) to effectively resolve both challenges. Both algorithms are equipped with a learning module for estimating the matching-reward information, while $\mathtt{DRAM}$ incorporates an additional module for learning the system dynamics. We show that both algorithms achieve an $O(\epsilon+\delta_r)$ close-to-optimal utility performance for any $\epsilon>0$, while $\mathtt{DRAM}$ achieves a faster convergence speed and a better delay compared to $\mathtt{LRAM}$, i.e., $O(\delta_{z}/\epsilon + \log(1/\epsilon)^2))$ delay and $O(\delta_z/\epsilon)$ convergence under $\mathtt{DRAM}$ compared to $O(1/\epsilon)$ delay and convergence under $\mathtt{LRAM}$ ($\delta_r$ and $\delta_z$ are maximum estimation errors for reward and system dynamics). Our results reveal that information of different system components can play very different roles in algorithm performance and provide a systematic way for designing joint learning-control algorithms for dynamic systems