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This paper presents a control law for a vertical jumping and landing motion with 4-link robot. A target system consists of four links and three actuators placed on each joint between the links. This system is fullactuated at the initial state or posture, but as the physical constraint varies in a process of a jumping motion, the system comes to be underactuated. With the control strategy overcoming these difficulties, we propose a control law of a vertical jumping and landing motion the the model, and numerical simulation.

This paper presents a control experiment of a vertical jumping motion with 4links Robot. A target system consists of four links and three actuators placed on each joint between the links. This system fullactuated at the initial step, but as the physical constraint varies in a process of jumping motion, the system comes to be underactuated. With the control strategy overcoming these difficulties, we conduct experiments with 4links Robot and show the experimental results.

This paper proposes a nonlinear controller realizing periodic motion to MonoRotor type flying robot having no equilibrium point. The control target is semi-hovering; a motion that passes a desired non-equilibrium point at which position and attitude stop periodically. In order to achieve this control objective, we propose discontinuous and time-varying output zeroing controller which ends up controlling Zero Dynamics. The numerical simulation and stability analysis show the validty of the suggesting controller design method.

Forming, in a decentralized fashion, an optimal network topology while balancing multiple, possibly conflicting objectives like cost, high performance, security and resiliency to viruses is a challenging endeavor. In this paper, we take a game-formation approach to network design where each player, for instance an autonomous system in the Internet, aims to collectively minimize the cost of installing links, of protecting against viruses, and of assuring connectivity. In the game, minimizing virus risk as well as connectivity costs results in sparse graphs. We show that the Nash Equilibria are trees that, according to the Price of Anarchy (PoA), are close to the global optimum, while the worst-case Nash Equilibrium and the global optimum may significantly differ for small infection rate and link installation cost. Moreover, the types of trees, in both the Nash Equilibria and the optimal solution, depend on the virus infection rate, which provides new insights into how viruses spread: for high infection rate τ, the path graph is the worst- and the star graph is the best-case Nash Equilibrium. However, for small and intermediate values of τ, trees different from the path and star graphs may be optimal.

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

Arctic sea ice is declining rapidly in extent and thickness, simplifying access to oil and gas fields, enabling trans-Arctic shipping and allowing storms to erode permafrost coasts. This in turn modifies the biogeochemical cycling of carbon and nutrients, intensifies land-ocean interactions, as well as it shifts the distribution of harvestable resources. Alarmingly, sea ice reductions are taking place more rapidly than predicted in any global climate model. This persistent mismatch between observed and predicted patterns makes planning activities in the Arctic challenging. Scientific knowledge of the evolving status of the Arctic Ocean, its surrounding land areas and the process-based understanding of the mechanics of change are urgently needed to make useful projections of future conditions throughout the Arctic region. Arctic in Rapid Transition (ART; http://www.iarc.uaf.edu/en/ART/) is an integrative, international, interdisciplinary, pan-Arctic network to study the spatial and temporal changes in biogeochemical cycling and ecosystem functions in the Arctic marine and coastal realm. ART has been developed and is still steered by early-career scientists. Since 2012 it is an official network of the International Arctic Science Committee (IASC). The first phase of ART (2010-2014) focuses on developing a formal network to bring together scientists working in different geographic and disciplinary areas who share a common interest in improving our understanding of Arctic change. An ART Science Workshop will be held 21-24 October 2014 in Plouzané, France, in collaboration with the Association of Polar Early Career Scientists (APECS), the Permafrost Young Researchers Network (PYRN) and the European Institute for Marine Studies. This international workshop entitled “Integrating spatial and temporal scales in the changing Arctic System: towards future research priorities” (ISTAS) will aim at drafting research priorities from an early to mid-career perspective that will feed into the third International Conference on Arctic Research Planning (ICARP III) in Toyama, Japan in 2015. This workshop will bring together about 60 early career, mid-career and senior scientists from different Arctic research areas including cryosphere, terrestrial, marine, atmosphere, and socio-economic topics to ensure knowledge transfer across generations and disciplines. The second phase of ART (2014-2018) will be centered on active data collection, such as through the TRANSSIZ expedition planned on the RV ‘Polarstern’ in 2015. The final phase of ART will be a synthesis stage, so that the legacy of ART will be a coherent set of knowledge, which would feed into physical-biological models at various scales in order to develop more robust scenarios regarding the future state of Arctic coastal and marine ecosystems, their productive capacity, how they impact the dynamics of greenhouse gases, as well as their role in global processes.