139,775 research outputs found
Nonconvex Distributed Feedback Optimization for Aggregative Cooperative Robotics
Distributed aggregative optimization is a recently emerged framework in which
the agents of a network want to minimize the sum of local objective functions,
each one depending on the agent decision variable (e.g., the local position of
a team of robots) and an aggregation of all the agents' variables (e.g., the
team barycentre). In this paper, we address a distributed feedback optimization
framework in which agents implement a local (distributed) policy to reach a
steady-state minimizing an aggregative cost function. We propose Aggregative
Tracking Feedback, i.e., a novel distributed feedback optimization law in which
each agent combines a closed-loop gradient flow with a consensus-based dynamic
compensator reconstructing the missing global information. By using tools from
system theory, we prove that Aggregative Tracking Feedback steers the network
to a stationary point of an aggregative optimization problem with (possibly)
nonconvex objective function. The effectiveness of the proposed method is
validated through numerical simulations on a multi-robot surveillance scenario
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High-Performance Integrated Window and Façade Solutions for California
The researchers developed a new generation of high-performance façade systems and supporting design and management tools to support industry in meeting California’s greenhouse gas reduction targets, reduce energy consumption, and enable an adaptable response to minimize real-time demands on the electricity grid. The project resulted in five outcomes: (1) The research team developed an R-5, 1-inch thick, triplepane, insulating glass unit with a novel low-conductance aluminum frame. This technology can help significantly reduce residential cooling and heating loads, particularly during the evening. (2) The team developed a prototype of a windowintegrated local ventilation and energy recovery device that provides clean, dry fresh air through the façade with minimal energy requirements. (3) A daylight-redirecting louver system was prototyped to redirect sunlight 15–40 feet from the window. Simulations estimated that lighting energy use could be reduced by 35–54 percent without glare. (4) A control system incorporating physics-based equations and a mathematical solver was prototyped and field tested to demonstrate feasibility. Simulations estimated that total electricity costs could be reduced by 9-28 percent on sunny summer days through adaptive control of operable shading and daylighting components and the thermostat compared to state-of-the-art automatic façade controls in commercial building perimeter zones. (5) Supporting models and tools needed by industry for technology R&D and market transformation activities were validated. Attaining California’s clean energy goals require making a fundamental shift from today’s ad-hoc assemblages of static components to turnkey, intelligent, responsive, integrated building façade systems. These systems offered significant reductions in energy use, peak demand, and operating cost in California
Culture change in elite sport performance teams: Examining and advancing effectiveness in the new era
Reflecting the importance of optimizing culture for elite teams, Fletcher and Arnold (2011) recently suggested the need for expertise in culture change. Acknowledging the dearth of literature on the specific process, however, the potential effectiveness of practitioners in this area is unknown. The present paper examines the activity's precise demands and the validity of understanding in sport psychology and organizational research to support its delivery. Recognizing that sport psychologists are being increasingly utilized by elite team management, initial evidence-based guidelines are presented. Finally, to stimulate the development of ecologically valid, practically meaningful knowledge, the paper identifies a number of future research directions
Near-Optimal Adversarial Policy Switching for Decentralized Asynchronous Multi-Agent Systems
A key challenge in multi-robot and multi-agent systems is generating
solutions that are robust to other self-interested or even adversarial parties
who actively try to prevent the agents from achieving their goals. The
practicality of existing works addressing this challenge is limited to only
small-scale synchronous decision-making scenarios or a single agent planning
its best response against a single adversary with fixed, procedurally
characterized strategies. In contrast this paper considers a more realistic
class of problems where a team of asynchronous agents with limited observation
and communication capabilities need to compete against multiple strategic
adversaries with changing strategies. This problem necessitates agents that can
coordinate to detect changes in adversary strategies and plan the best response
accordingly. Our approach first optimizes a set of stratagems that represent
these best responses. These optimized stratagems are then integrated into a
unified policy that can detect and respond when the adversaries change their
strategies. The near-optimality of the proposed framework is established
theoretically as well as demonstrated empirically in simulation and hardware
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