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In this paper, we present a novel learning-aided energy management scheme ( $\mathtt {LEM}$ LEM ) for multihop energy harvesting networks. Different from prior works on this problem, our algorithm explicitly incorporates information learning into system control via a step called perturbed dual learning . $\mathtt {LEM}$ LEM does not require any statistical information of the system dynamics for implementation, and efficiently resolves the challenging energy outage problem. We show that $\mathtt {LEM}$ LEM achieves the near-optimal $[O(\epsilon), O(\log (1/\epsilon)^2)]$ [ O ( e ) , O ( log ( 1 / e ) 2 ) ] utility-delay tradeoff with an $O(1/\epsilon ^{1-c/2})$ O ( 1 / e 1 - c / 2 ) energy buffers ( $c\in (0,1)$ c ∈ ( 0 , 1 ) ). More interestingly, $\mathtt {LEM}$ LEM possesses a convergence time of $O(1/\epsilon ^{1-c/2} +1/\epsilon ^c)$ O ( 1 / e 1 - c / 2 + 1 / e c ) , which is much faster than the $\Theta (1/\epsilon)$ Θ ( 1 / e ) time of pure queue-based techniques or the $\Theta (1/\epsilon ^2)$ Θ ( 1 / e 2 ) time of approaches that rely purely on learning the system statistics. This fast convergence property makes $\mathtt {LEM}$ LEM more adaptive and efficient in resource allocation in dynamic environments. The design and analysis of $\mathtt {LEM}$ LEM demonstrate how system control algorithms can be augmented by learning and what the benefits are. The methodology and algorithm can also be applied to similar problems, e.g., processing networks, where nodes require nonzero contents to support their actions.

In this paper, we propose a reachable set based collision avoidance algorithm for unmanned aerial vehicles (UAVs). UAVs have been deployed for agriculture research and management, surveillance and sensor coverage for threat detection and disaster search and rescue operations. It is essential for the aircraft to have on-board collision avoidance capability to guarantee safety. Instead of the traditional approach of collision avoidance between trajectories, we propose a collision avoidance scheme based on reachable sets and tubes. We then formulate the problem as a convex optimization problem seeking suitable control constraint sets for participating aircraft. We have applied the approach on a case study of two quadrotors and two fix-wing aircraft collision avoidance scenario., Comment: CDC 2015 fixed-wing nonlinear dynamics extension. CDC 2015 DOI: 10.1109/CDC.2015.7403154

The identification of structured state-space model has been intensively studied for a long time but still has not been adequately addressed. The main challenge is that the involved estimation problem is a non-convex (or bilinear) optimization problem. This paper is devoted to developing an identification method which aims to find the global optimal solution under mild computational burden. Key to the developed identification algorithm is to transform a bilinear estimation to a rank constrained optimization problem and further a difference of convex programming (DCP) problem. The initial condition for the DCP problem is obtained by solving its convex part of the optimization problem which happens to be a nuclear norm regularized optimization problem. Since the nuclear norm regularized optimization is the closest convex form of the low-rank constrained estimation problem, the obtained initial condition is always of high quality which provides the DCP problem a good starting point. The DCP problem is then solved by the sequential convex programming method. Finally, numerical examples are included to show the effectiveness of the developed identification algorithm., Accepted to IEEE Conference on Decision and Control (CDC) 2015

International audience; A nonlinear observer on the Special Euclidean group SE(3) for full pose estimation, that takes the system outputs on the real projective space directly as inputs, is proposed. The observer derivation is based on a recent advanced theory on nonlinear observer design. A key advantage with respect to existing pose observers on SE(3) is that we can now incorporate in a unique observer different types of measurements such as vectorial measurements of known inertial vectors and position measurements of known feature points. The proposed observer is extended allowing for the compensation of unknown constant bias present in the velocity measurements. Rigorous stability analyses are equally provided. Excellent performance of the proposed observers are shown by means of simulations.