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Summary We present a novel distributed primal‐dual active‐set method for model predictive control. The primal‐dual active‐set method is used for solving model predictive control problems for large‐scale systems with quadratic cost, linear dynamics, additive disturbance, and box constraints. The proposed algorithm is compared with dual decomposition and an alternating direction method of multipliers. Theoretical and experimental results show the effectiveness of the proposed approach for large‐scale systems with communication delays. The application to building control systems is thoroughly investigated. Copyright © 2016 John Wiley & Sons, Ltd.

While both semantic and highly emotional (i.e., taboo) words can interfere with speech production, different theoretical mechanisms have been proposed to explain why interference occurs. Two experiments investigated these theoretical approaches by comparing the magnitude of these two types of interference and the stages at which they occur during picture naming. Participants named target pictures superimposed with semantic, taboo, or unrelated distractor words that were presented at three different stimulus-onset asynchronies (SOA): −150 ms, 0 ms, or +150 ms. In addition, the duration of distractor presentation was manipulated across experiments, with distractors appearing for the duration of the picture (Experiment 1) or for 350 ms (Experiment 2). Taboo distractors interfered more than semantic distractors, i.e., slowed target naming times, at all SOAs. While distractor duration had no effect on type of interference at −150 or 0 SOAs, briefly presented distractors eliminated semantic interference but not taboo interference at +150 SOA. Discussion focuses on how existing speech production theories can explain interference from emotional distractors and the unique role that attention may play in taboo interference.

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Titan, one of Saturn's moons, is an area of high interest for in-situ study due to its many intriguing features such as cryovolcanos and methane lakes. This Saturnian moon has a dense atmosphere, rough icy terrain, and low surface winds which would make the ideal place to send a controlled aerial robotic platform such as an Aerobot Airship. An important feature of a self-propelled lighter-than-air aerial vehicle is that it must be autonomously controlled to navigate and avoid obstacles due to a 2.6 hour communication delay that exists between the Earth and Titan. Developing a dynamic model that could be tuned wouldenable robust and reliable control of the Aerobot Airship. A parameterized analytical longitudinal linear dynamic model of the airship has been previously developed in support of Autonomous Aerobots. This paper presents the development of a linear parameterized analytical lateral dynamic model for an airship, completing the three-dimensional motion of an airship. The method used to develop this dynamic model does not utilize system identification as is typically used, but it relies on the use of aircraft stability derivative methods with the basic geometric and aerodynamic properties of the airship. This method reduces time and cost while providing ease of implementation fortuning multiple operating points. Comparisons of simulations of the model with other stable airship dynamic models show similar behavior that validates our method. Nomenclature A= 6x1 aerodynamics force vector A = state matrix for state space AR = aspect ratio of the main lifting body (unitless) B = buoyancy force acting at the center of volume (N) B= input matrix for state space C L = coefficient of lift (unitless) = vertical tail lift curve slope (unitless) = coefficient of the change in L moment with respect to sideslip angle β (unitless) = tip shape effect on (unitless) = coefficient of the change in L moment with respect to roll rate p (unitless) = coefficient of the change in L moment with respect to yaw rate r (unitless) = coefficient of the change in L moment with respect to rudder deflection (unitless) = coefficient of the change in N moment with respect to sideslip angle β (unitless) = coefficient of the change in N moment due to the wing fuselage with respect to sideslip angle β = coefficient of the change in N moment with respect to roll rate p (unitless) = coefficient of the change in N moment with respect to yaw rate r (unitless) = coefficient of the change in N moment with respect to rudder deflection (unitless) = coefficient of the change in Y force with respect to sideslip angle β (unitless) = coefficient of the change in Y force at the tail with respect to sideslip angle β (unitless) = coefficient of the change in Y force with respect to roll rate p (unitless) = coefficient of the change in Y force with respect to yaw rate r (unitless) = coefficient of the change in Y force with respect to rudder deflection (unitless) F d = 6x1 dynamics force vector G = 6x1 gravitational and buoyancy forces vector I x ,I z = actual ...

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Reconfigurable modular robots can exhibit different specializations by rearranging the same set of parts comprising them. Actuating modular robots can be complicated because of the many degrees of freedom that scale exponentially with the size of the robot. Effectively controlling these robots directly relates to how well they can be used to complete meaningful tasks. This paper discusses an approach for creating provably correct controllers for modular robots from high-level tasks defined with structured English sentences. While this has been demonstrated with simple mobile robots, the problem was enriched by considering the uniqueness of reconfigurable modular robots. These requirements are expressed through traits in the high-level task specification that store information about the geometry and motion types of a robot.Given a high-level problem definition for a modular robot, the approach in this paper deals with generating all lower levels of control needed to solve it. Information about different robot characteristics is stored in a library, and two tools for populating this library have been developed. The first approach is a physics-based simulator and gait creator for manual generation of motion gaits. The second is a genetic algorithm framework that uses traits to evaluate performance under various metrics. Demonstration is done through simulation and with the CKBot hardware platform.